This simply updates the rewrites to propagate the scalable flags (which
as they do not alter the vector shape, is pretty simple).
The added tests are simply scalable versions of the existing vector
tests.
The `GreedyPatternRewriteDriver` tries to iteratively fold ops and apply
rewrite patterns to ops. It has special handling for constants: they are
CSE'd and sometimes moved to parent regions to allow for additional
CSE'ing. This happens in `OperationFolder`.
To allow for efficient CSE'ing, `OperationFolder` maintains an internal
lookup data structure to find the existing constant ops with the same
value for each `IsolatedFromAbove` region:
```c++
/// A mapping between an insertion region and the constants that have been
/// created within it.
DenseMap<Region *, ConstantMap> foldScopes;
```
Rewrite patterns are allowed to modify operations. In particular, they
may move operations (including constants) from one region to another
one. Such an IR rewrite can make the above lookup data structure
inconsistent.
We encountered such a bug in a downstream project. This bug materialized
in the form of an op that uses the result of a constant op from a
different `IsolatedFromAbove` region (that is not accessible).
This commit changes the behavior of the `GreedyPatternRewriteDriver`
such that `OperationFolder` is used to CSE constants at the beginning of
each iteration (as the worklist is populated), but no longer during an
iteration. `OperationFolder` is no longer used after populating the
worklist, so we do not have to care about inconsistent state in the
`OperationFolder` due to IR rewrites. The `GreedyPatternRewriteDriver`
now performs the op folding by itself instead of calling
`OperationFolder::tryToFold`.
This change changes the order of constant ops in test cases, but not the
region in which they appear. All broken test cases were fixed by turning
`CHECK` into `CHECK-DAG`.
Alternatives considered: The state of `OperationFolder` could be
partially invalidated with every `notifyOperationModified` notification.
That is more fragile than the solution in this commit because incorrect
rewriter API usage can lead to missing notifications and hard-to-debug
`IsolatedFromAbove` violations. (It did not fix the above mention bug in
a downstream project, which could be due to incorrect rewriter API usage
or due to another conceptual problem that I missed.) Moreover, ops are
frequently getting modified during a greedy pattern rewrite, so we would
likely keep invalidating large parts of the state of `OperationFolder`
over and over.
Migration guide: Turn `CHECK` into `CHECK-DAG` in test cases. Constant
ops are no longer folded during a greedy pattern rewrite. If you rely on
folding (and rematerialization) of constant ops during a greedy pattern
rewrite, turn the folder into a pattern.
Used the cephes numerical approximation for `math.atan`. This is a
significant accuracy improvement over the previous taylor series
approximation.
Reviewed By: jpienaar
Differential Revision: https://reviews.llvm.org/D153656
The existing lowering has lower precision for certain use cases, e.g.
tanh. Improved version should demonstrate an overall higher level of precision.
Reviewed By: cota, jpienaar
Differential Revision: https://reviews.llvm.org/D153592
Polynomial approximations assume F32 values. We can convert all non-f32
cases to operate on f32s with intermediate casts.
Reviewed By: jpienaar
Differential Revision: https://reviews.llvm.org/D146677
Cbrt can be approximated with some relatively simple polynomial
operators. This includes a lit test validating the implementation
and some run tests that validate numerical correct.
Reviewed By: jpienaar
Differential Revision: https://reviews.llvm.org/D145019
Previously, NaNs would be dropped in favor of bounded values which was
strictly incorrect. Now the min/max operation propagate this
information. Not all uses of min/max need this, but the given change
will help protect future additions, and this prevents the need for an
additional cmpf and select operation to handle NaNs.
Differential Revision: https://reviews.llvm.org/D120020
Reuse the higher precision F32 approximation for the F16 one (by expanding and
truncating). This is partly RFC as I'm not sure what the expectations are here
(e.g., these are only for F32 and should not be expanded, that reusing
higher-precision ones for lower precision is undesirable due to increased
compute cost and only approximations per exact type is preferred, or this is
appropriate [at least as fallback] but we need to see how to make it more
generic across all the patterns here).
Differential Revision: https://reviews.llvm.org/D118968
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
Polynomial approximation can be extented to support N-d vectors.
N-dimensional vectors are useful when vectorizing operations on N-dimensional
tiles. Before lowering to LLVM these vectors are usually unrolled or flattened
to 1-dimensional vectors.
Differential Revision: https://reviews.llvm.org/D112566
This patch adds a polynomial approximation that matches the
approximation in Eigen.
Note that the approximation only applies to vectorized inputs;
the scalar rsqrt is left unmodified.
The approximation is protected with a flag since it emits an AVX2
intrinsic (generated via the X86Vector). This is the only reasonably
clean way that I could find to generate the exact approximation that
I wanted (i.e. an identical one to Eigen's).
I considered two alternatives:
1. Introduce a Rsqrt intrinsic in LLVM, which doesn't exist yet.
I believe this is because there is no definition of Rsqrt that
all backends could agree on, since hardware instructions that
implement it have widely varying degrees of precision.
This is something that the standard could mandate, but Rsqrt is
not part of IEEE754, so I don't think this option is feasible.
2. Emit fdiv(1.0, sqrt) with fast math flags to allow reciprocal
transformations. Although portable, this doesn't allow us
to generate exactly the code we want; it is the LLVM backend,
and not MLIR, who controls what code is generated based on the
target CPU.
Reviewed By: ezhulenev
Differential Revision: https://reviews.llvm.org/D112192
Use wider range for approximating Tanh to match results computed in Eigen with AVX.
Reviewed By: cota
Differential Revision: https://reviews.llvm.org/D112011
Precursor: https://reviews.llvm.org/D110200
Removed redundant ops from the standard dialect that were moved to the
`arith` or `math` dialects.
Renamed all instances of operations in the codebase and in tests.
Reviewed By: rriddle, jpienaar
Differential Revision: https://reviews.llvm.org/D110797
This is a bit cleaner and removes issues with 2d vectors. It also has a
big impact on constant folding, hence the test changes.
Differential Revision: https://reviews.llvm.org/D107896
