Split arithmetic function into unary and binary functions. The revision prepares the introduction of unary and binary function attributes that work similar to type function attributes.
Depends On D120108
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D120109
This patch refactors the looping strategy of coalesce for future patches. The new strategy works in-place and uses IneqType to organize inequalities into vectors of the same type. Future coalesce cases will pattern match on this organization. E.g. the contained case needs all inequalities and equalities to be redundant, so this case becomes checking whether the respective vectors are empty. For other cases, the patterns consider the types of all inequalities of both sets making it wasteful to only consider whether a can be coalesced with b in one step, as inequalities would need to be typed again for the opposite case. Therefore, the new strategy tries to coalesce a with b and b with a in a single step.
Reviewed By: Groverkss, arjunp
Differential Revision: https://reviews.llvm.org/D120392
This patch replaces various functions over inequalities/equalities in
IntegerPolyhedron with Matrix functions already implementing them or refactors
them to a Matrix function.
Reviewed By: arjunp
Differential Revision: https://reviews.llvm.org/D120482
This patch moves the Presburger library to a new `presburger` namespace.
This allows to shorten some names, helps to avoid polluting the mlir namespace,
and also provides some structure.
Reviewed By: arjunp
Differential Revision: https://reviews.llvm.org/D120505
This patch removes redundant code from fourierMotzkinEliminate implementation
using existing functions in IntegerPolyhedron.
Reviewed By: arjunp
Differential Revision: https://reviews.llvm.org/D120502
Previously, OpDSL operation used hardcoded type conversion operations (cast or cast_unsigned). Supporting signed and unsigned casts thus meant implementing two different operations. Type function attributes allow us to define a single operation that has a cast type function attribute which at operation instantiation time may be set to cast or cast_unsigned. We may for example, defina a matmul operation with a cast argument:
```
@linalg_structured_op
def matmul(A=TensorDef(T1, S.M, S.K), B=TensorDef(T2, S.K, S.N), C=TensorDef(U, S.M, S.N, output=True),
cast=TypeFnAttrDef(default=TypeFn.cast)):
C[D.m, D.n] += cast(U, A[D.m, D.k]) * cast(U, B[D.k, D.n])
```
When instantiating the operation the attribute may be set to the desired cast function:
```
linalg.matmul(lhs, rhs, outs=[out], cast=TypeFn.cast_unsigned)
```
The revsion introduces a enum in the Linalg dialect that maps one-by-one to the type functions defined by OpDSL.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D119718
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 adds a variable op, emitted as C/C++ locale variable, which can be
used if the `emitc.constant` op is not sufficient.
As an example, the canonicalization pass would transform
```mlir
%0 = "emitc.constant"() {value = 0 : i32} : () -> i32
%1 = "emitc.constant"() {value = 0 : i32} : () -> i32
%2 = emitc.apply "&"(%0) : (i32) -> !emitc.ptr<i32>
%3 = emitc.apply "&"(%1) : (i32) -> !emitc.ptr<i32>
emitc.call "write"(%2, %3) : (!emitc.ptr<i32>, !emitc.ptr<i32>) -> ()
```
into
```mlir
%0 = "emitc.constant"() {value = 0 : i32} : () -> i32
%1 = emitc.apply "&"(%0) : (i32) -> !emitc.ptr<i32>
%2 = emitc.apply "&"(%0) : (i32) -> !emitc.ptr<i32>
emitc.call "write"(%1, %2) : (!emitc.ptr<i32>, !emitc.ptr<i32>) -> ()
```
resulting in pointer aliasing, as %1 and %2 point to the same address.
In such a case, the `emitc.variable` operation can be used instead.
Reviewed By: jpienaar
Differential Revision: https://reviews.llvm.org/D120098
The current implementation of ShuffleVectorOp assumes all vectors are
scalable. LLVM IR allows shufflevector operations on scalable vectors,
and the current translation between LLVM Dialect and LLVM IR does the
rigth thing when the shuffle mask is all zeroes. This is required to
do a splat operation on a scalable vector, but it doesn't make sense
for scalable vectors outside of that operation, i.e.: with non-all zero
masks.
Differential Revision: https://reviews.llvm.org/D118371
In D115022, we introduced an optimization where OpResults of a `linalg.generic` may bufferize in-place with an "in" OpOperand if the corresponding "out" OpOperand is not used in the computation.
This optimization can lead to unexpected behavior if the newly chosen OpOperand is in the same alias set as another OpOperand (that is used in the computation). In that case, the newly chosen OpOperand must bufferize out-of-place. This can be confusing to users, as always choosing the "out" OpOperand (regardless of whether it is used) would be expected when having the notion of "destination-passing style" in mind.
With this change, we go back to always bufferizing in-place with "out" OpOperands by default, but letting users override the behavior with a bufferization option.
Differential Revision: https://reviews.llvm.org/D120182
Previously only accessing values for `index` and signless int types
would work; signed and unsigned ints would hit an assert in
`IntegerAttr::getInt`. This exposes `IntegerAttr::get{S,U}Int` to the C
API and calls the appropriate function from the python bindings.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D120194
Now that sparse tensor types are first-class citizens and the sparse compiler
is taking shape, it is time to make sure other compiler optimizations compose
well with sparse tensors. Mostly, this should be completely transparent (i.e.,
dense and sparse take the same path). However, in some cases, optimizations
only make sense in the context of sparse tensors. This is a first example of
such an optimization, where fusing a sampled elt-wise multiplication only makes
sense when the resulting kernel has a potential lower asymptotic complexity due
to the sparsity.
As an extreme example, running SDDMM with 1024x1024 matrices and a sparse
sampling matrix with only two elements runs in 463.55ms in the unfused
case but just 0.032ms in the fused case, with a speedup of 14485x that
is only possible in the exciting world of sparse computations!
Reviewed By: mravishankar
Differential Revision: https://reviews.llvm.org/D120429
By specifying a sectionMemoryMapper, users can control how
memory for JIT code is allocated.
In particular, I need this in order to use a named memory
region so that profilers such as perf(1) can correctly label
execution cycles coming from JIT'ed code.
Reviewed-by: ezhulenev
Differential Revision: https://reviews.llvm.org/D120415
Given a cmpf of either uitofp or sitofp and a constant, attempt to canonicalize it to a cmpi.
This PR rewrites equivalent code within LLVM to now apply to MLIR arith.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D117257
+ compare block size with the unrollable inner dimension
+ reduce nesting in the code and simplify a bit IR building
Reviewed By: cota
Differential Revision: https://reviews.llvm.org/D120075
Its number of optional parameters has grown too large,
which makes adding new optional parameters quite a chore.
Fix this by using an options struct.
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D120380
The related functionality is moved over to the bufferization dialect. Test cases are cleaned up a bit.
Differential Revision: https://reviews.llvm.org/D120191
This commit adds canonicalization pattern in `linalg.generic` op
for static shape inference. If any of the inputs or outputs have
static shape or is casted from a tensor of static shape, then
shapes of all the inputs and outputs can be inferred by using the
affine map of the static shape input/output.
Signed-Off-By: Prateek Gupta <prateek@nod-labs.com>
Reviewed By: mravishankar
Differential Revision: https://reviews.llvm.org/D118929
This patch adds assemblyFormat for omp.sections operation.
Some existing functions have been altered to fit the custom directive
in assemblyFormat. This has led to their callsites to get modified too,
but those will be removed in later patches, when other operations get
their assemblyFormat. All operations were not changed in one patch for
ease of review.
Reviewed By: Mogball
Differential Revision: https://reviews.llvm.org/D120176
This patch adds typing of inequalities to the simplex. This is a cental part of the coalesce algorithm and will be heavily used in later coalesce patches. Currently, only the three most basic types are supported with more to be introduced when they are needed.
Reviewed By: arjunp
Differential Revision: https://reviews.llvm.org/D119925
This allows to differentiate between the cases where the optimum does not
exist due to being unbounded and due to the polytope being empty.
Reviewed By: Groverkss
Differential Revision: https://reviews.llvm.org/D120127
Add `BufferizableOpInterface::verifyAnalysis`. Ops can implement this method to check for expected invariants and limitations.
The purpose of this change is to introduce a modular way of checking assertions such as `assertScfForAliasingProperties`.
Differential Revision: https://reviews.llvm.org/D120189
This patch adds assemblyFormat for omp.parallel operation.
Some existing functions have been altered to fit the custom directive
in assemblyFormat. This has led to their callsites to get modified too,
but those will be removed in later patches, when other operations get
their assemblyFormat. All operations were not changed in one patch for
ease of review.
Reviewed By: Mogball
Differential Revision: https://reviews.llvm.org/D120157
These routines will need to be specialized a lot more based on value types,
index types, pointer types, and permutation/dimension ordering. This is a
careful first step, providing some functionality needed in PyTACO bridge.
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D120154
This patch removes the following clauses from OpenMP Dialect:
- private
- firstprivate
- lastprivate
- shared
- default
- copyin
- copyprivate
The privatization clauses are being handled in the flang frontend. The
data copying clauses are not being handled anywhere for now. Once
we have a better picture of how to handle these clauses in OpenMP
Dialect, we can add these. For the time being, removing unneeded
clauses.
For detailed discussion about this refer to [[ https://discourse.llvm.org/t/rfc-privatisation-in-openmp-dialect/3526 | Privatisation in OpenMP dialect ]]
Reviewed By: kiranchandramohan, clementval
Differential Revision: https://reviews.llvm.org/D120029
This patch introducing seperating dimensions into two types: Domain and Range.
This allows building relations over PresburgerSpace.
This patch is part of a series of patches to introduce relations in Presburger
library.
Reviewed By: arjunp
Differential Revision: https://reviews.llvm.org/D119709
insert is soft deprecated, so remove all references so it's less likely
to be used and can be easily removed in the future.
Differential Revision: https://reviews.llvm.org/D120021
This is a bit awkward since ExtractOp allows both `f32` and
`vector<1xf32>` results for a scalar extraction. Allow both, but make
inference return the scalar to make this as NFC as possible.
This change changes the handling of trailing dimensions with unknown
extent. Users of the changessociationIndicesForReshape helper should
see benefits when transforming reshape like operations into
expand/collapse pairs if the higher-rank type has trailing unknown
dimensions.
The motivating example is a reshape from tensor<16x1x?xi32> to
tensor<16xi32> that can be modeled as collapsing the three dimensions.
Differential Revision: https://reviews.llvm.org/D119730
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
This op is added to allow MLIR code running on multi-GPU systems to
select the GPU they want to execute operations on when no GPU is
otherwise specified.
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D119883
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