This patch mechanically replaces None with std::nullopt where the
compiler would warn if None were deprecated. The intent is to reduce
the amount of manual work required in migrating from Optional to
std::optional.
This is part of an effort to migrate from llvm::Optional to
std::optional:
https://discourse.llvm.org/t/deprecating-llvm-optional-x-hasvalue-getvalue-getvalueor/63716
Small change to support projected permutations in the
`getPermutedPosition` utility. Renamed to `getResultPosition`.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D138946
Simplify affine expressions and maps while exploiting simple range and
step info of any IVs that are operands. This simplification is local,
O(1) and practically useful in several scenarios. Accesses with
floordiv's and mod's where the LHS is non-negative and bounded or is a
known multiple of a constant can often be simplified. This is
implemented as a canonicalization for all affine ops in a generic way:
all affine.load/store, vector_load/store, affine.apply, affine.min/max,
etc. ops.
Eg: For tiled loop nests accessing buffers this way:
affine.for %i = 0 to 1024 step 32 {
affine.for %ii = 0 to 32 {
affine.load [(%i + %ii) floordiv 32, (%i + %ii) mod 32]
}
}
// Note that %i is a multiple of 32 and %ii < 32, hence:
(%i + %ii) floordiv 32 is the same as %i floordiv 32
(%i + %ii) mod 32 is the same as %ii mod 32.
The simplification leads to simpler index/subscript arithmetic for
multi-dimensional arrays and also in turn enables detection of spatial
locality (for vectorization for eg.), temporal locality or loop
invariance for hoisting or scalar replacement.
Differential Revision: https://reviews.llvm.org/D135085
Bubble up extract_slice above Linalg operation.
A sequence of operations
%0 = linalg.<op> ... arg0, arg1, ...
%1 = tensor.extract_slice %0 ...
can be replaced with
%0 = tensor.extract_slice %arg0
%1 = tensor.extract_slice %arg1
%2 = linalg.<op> ... %0, %1, ...
This results in the reduce computation of the linalg operation.
The implementation uses the tiling utility functions. One difference
from the tiling process is that we don't need to insert the checking
code for the out-of-bound accesses. The use of the slice itself
represents that the code writer is sure about the boundary condition.
To avoid adding the boundary condtion check code, `omitPartialTileCheck`
is introduced for the tiling utility functions.
Differential Revision: https://reviews.llvm.org/D122437
This is both more efficient and more ergonomic to use, as inverting a
bit vector is trivial while inverting a set is annoying.
Sadly this leaks into a bunch of APIs downstream, so adapt them as well.
This would be NFC, but there is an ordering dependency in MemRefOps's
computeMemRefRankReductionMask. This is now deterministic, previously it
was dependent on SmallDenseSet's unspecified iteration order.
Differential Revision: https://reviews.llvm.org/D119076
We check whether the maximum index of dimensional identifier present
in the result expressions is less than dimCount (number of dimensional
identifiers) argument passed in the AffineMap::get() and the maximum index
of symbolic identifier present in the result expressions is less than
symbolCount (number of symbolic identifiers) argument passed in AffineMap::get().
Reviewed By: nicolasvasilache, bondhugula
Differential Revision: https://reviews.llvm.org/D114238
This patch teaches `isProjectedPermutation` and `inverseAndBroadcastProjectedPermutation`
utilities to deal with maps representing an explicit broadcast, e.g., (d0, d1) -> (d0, 0).
This extension is needed to enable vectorization of such explicit broadcast in Linalg.
Reviewed By: pifon2a, nicolasvasilache
Differential Revision: https://reviews.llvm.org/D111563
I found myself typing this code several times at different places
by now, so time to make this a general utility instead. Given
a permutation, it returns the permuted position of the input,
for example (i,j,k) -> (k,i,j) yields position 1 for input 0.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D108347
This pattern inlines operands to a linalg.generic operation that use a constant
index and hence are loop-invariant scalars. This reduces the number of
linalg.generic operands and unlocks some canonicalizations that rely on seeing
an explicit tensor.extract.
Differential Revision: https://reviews.llvm.org/D102682
The current implementation had a bug as it was relying on the target vector
dimension sizes to calculate where to insert broadcast. If several dimensions
have the same size we may insert the broadcast on the wrong dimension. The
correct broadcast cannot be inferred from the type of the source and
destination vector.
Instead when we want to extend transfer ops we calculate an "inverse" map to the
projected permutation and insert broadcast in place of the projected dimensions.
Differential Revision: https://reviews.llvm.org/D101738
This enables to express more complex parallel loops in the affine framework,
for example, in cases of tiling by sizes not dividing loop trip counts perfectly
or inner wavefront parallelism, among others. One can't use affine.max/min
and supply values to the nested loop bounds since the results of such
affine.max/min operations aren't valid symbols. Making them valid symbols
isn't an option since they would introduce selection trees into memref
subscript arithmetic as an unintended and undesired consequence. Also
add support for converting such loops to SCF. Drop some API that isn't used in
the core repo from AffineParallelOp since its semantics becomes ambiguous in
presence of max/min bounds. Loop normalization is currently unavailable for
such loops.
Depends On D101171
Reviewed By: bondhugula
Differential Revision: https://reviews.llvm.org/D101172
This revision tightens up the handling of attributes for both named
and generic linalg ops.
To demonstrate the IR validity, a working e2e Linalg example is added.
Differential Revision: https://reviews.llvm.org/D99430
Convert transfer_read ops with permutation maps into simpler
transfer_read with minority map + vector.braodcast and vector.transpose.
And transfer_read with leading dimensions broacast into transfer_read of
lower rank.
Differential Revision: https://reviews.llvm.org/D99019
This patch introduces progressive lowering patterns for rewriting
vector.transfer_read/write to vector.load/store and vector.broadcast
in certain supported cases.
Reviewed By: dcaballe, nicolasvasilache
Differential Revision: https://reviews.llvm.org/D97822
The AffineMap in the MemRef inferred by SubViewOp may have uncompressed symbols which result in type mismatch on otherwise unused symbols. Make the computation of the AffineMap compress those unused symbols which results in better canonical types.
Additionally, improve the error message to report which inferred type was expected.
Differential Revision: https://reviews.llvm.org/D96551
The `AffineMap` class follows the same semantic as Type and Attribute.
It is immutable object, so it make sence to mark its methods as const.
Also part of its API is already marked as const, this change just make the API consistent.
Reviewed By: ftynse, bondhugula
Differential Revision: https://reviews.llvm.org/D96026
In prehistorical times, AffineApplyOp was allowed to produce multiple values.
This allowed the creation of intricate SSA use-def chains.
AffineApplyNormalizer was originally introduced as a means of reusing the AffineMap::compose method to write SSA use-def chains.
Unfortunately, symbols that were produced by an AffineApplyOp needed to be promoted to dims and reordered for the mathematical composition to be valid.
Since then, single result AffineApplyOp became the law of the land but the original assumptions were not revisited.
This revision revisits these assumptions and retires AffineApplyNormalizer.
Differential Revision: https://reviews.llvm.org/D94920
This is part of a larger refactoring the better congregates the builtin structures under the BuiltinDialect. This also removes the problematic "standard" naming that clashes with the "standard" dialect, which is not defined within IR/. A temporary forward is placed in StandardTypes.h to allow time for downstream users to replaced references.
Differential Revision: https://reviews.llvm.org/D92435
motivated by a refactoring in the new sparse code (yet to be merged), this avoids some lengthy code dup
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D91465
This change does two main things
1) An operation might have multiple dependences to the same
producer. Not tracking them correctly can result in incorrect code
generation with fusion. To rectify this the dependence tracking
needs to also have the operand number in the consumer.
2) Improve the logic used to find the fused loops making it easier to
follow. The only constraint for fusion is that linalg ops (on
buffers) have update semantics for the result. Fusion should be
such that only one iteration of the fused loop (which is also a
tiled loop) must touch only one (disjoint) tile of the output. This
could be relaxed by allowing for recomputation that is the default
when oeprands are tensors, or can be made legal with promotion of
the fused view (in future).
Differential Revision: https://reviews.llvm.org/D90579
This commit adds functionality needed for implementation of convolutions with
linalg.generic op. Since linalg.generic right now expects indexing maps to be
just permutations, offset indexing needed in convolutions is not possible.
Therefore in this commit we address the issue by adding support for symbols inside
indexing maps which enables more advanced indexing. The upcoming commit will
solve the problem of computing loop bounds from such maps.
Differential Revision: https://reviews.llvm.org/D83158
This revision folds vector.transfer operations by updating the `masked` bool array attribute when more unmasked dimensions can be discovered.
Differential revision: https://reviews.llvm.org/D83586
TransposeOp are often followed by ExtractOp.
In certain cases however, it is unnecessary (and even detrimental) to lower a TransposeOp to either a flat transpose (llvm.matrix intrinsics) or to unrolled scalar insert / extract chains.
Providing foldings of ExtractOp mitigates some of the unnecessary complexity.
Differential revision: https://reviews.llvm.org/D83487
This revision adds foldings for ExtractOp operations that come from previous InsertOp.
InsertOp have cumulative semantic where multiple chained inserts are necessary to produce the final value from which the extracts are obtained.
Additionally, TransposeOp may be interleaved and need to be tracked in order to follow the producer consumer relationships and properly compute positions.
Differential revision: https://reviews.llvm.org/D83150
This is consistent to the other methods of the class, as well as
AffineExpr::replaceDimsAndSymbols.
Differential Revision: https://reviews.llvm.org/D80266
Originally, these operations were folded only if all expressions in their
affine maps could be folded to a constant expression that can be then subject
to numeric min/max computation. This introduces a more advanced version that
partially folds the affine map by lifting individual constant expression in it
even if some of the expressions remain variable. The folding can update the
operation in place to use a simpler map. Note that this is not as powerful as
canonicalization, in particular this does not remove dimensions or symbols that
became useless. This allows for better composition of Linalg tiling and
promotion transformation, where the latter can handle some canonical forms of
affine.min that the folding can now produce.
Differential Revision: https://reviews.llvm.org/D79502
This revision allows masked vector transfers with m-D buffers and n-D vectors to
progressively lower to m-D buffer and 1-D vector transfers.
For a vector.transfer_read, assuming a `memref<(leading_dims) x (major_dims) x (minor_dims) x type>` and a `vector<(minor_dims) x type>` are involved in the transfer, this generates pseudo-IR resembling:
```
if (any_of(%ivs_major + %offsets, <, major_dims)) {
%v = vector_transfer_read(
{%offsets_leading, %ivs_major + %offsets_major, %offsets_minor},
%ivs_minor):
memref<(leading_dims) x (major_dims) x (minor_dims) x type>,
vector<(minor_dims) x type>;
} else {
%v = splat(vector<(minor_dims) x type>, %fill)
}
```
Differential Revision: https://reviews.llvm.org/D79062
Summary:
Modified AffineMap::get to remove support for the overload which allowed
an ArrayRef of AffineExpr but no context (and gathered the context from a
presumed first entry, resulting in bugs when there were 0 results).
Instead, we support only a ArrayRef and a context, and a version which
takes a single AffineExpr.
Additionally, removed some now needless case logic which previously
special cased which call to AffineMap::get to use.
Reviewers: flaub, bondhugula, rriddle!, nicolasvasilache, ftynse, ulysseB, mravishankar, antiagainst, aartbik
Subscribers: mehdi_amini, jpienaar, burmako, shauheen, antiagainst, arpith-jacob, mgester, lucyrfox, liufengdb, Joonsoo, bader, grosul1, frgossen, llvm-commits
Tags: #llvm
Differential Revision: https://reviews.llvm.org/D78226
Summary: Functional.h contains many different methods that have a direct, and more efficient, equivalent in LLVM. This revision replaces all usages with the LLVM equivalent, and removes the header. This is part of larger cleanup, pr45513, merging MLIR support facilities into LLVM.
Differential Revision: https://reviews.llvm.org/D78053
Add a method that given an affine map returns another with just its unique
results. Use this to drop redundant bounds in max/min for affine.for. Update
affine.for's canonicalization pattern and createCanonicalizedForOp to use
this.
Differential Revision: https://reviews.llvm.org/D77237
This revision takes advantage of the empty AffineMap to specify the
0-D edge case. This allows removing a bunch of annoying corner cases
that ended up impacting users of Linalg.
Differential Revision: https://reviews.llvm.org/D75831
Fixing a bug where using a zero-rank shaped type operand to
linalg.generic ops hit an unrelated assert. This also meant that
lowering the operation to loops was not supported. Adding roundtrip
tests and lowering to loops test for zero-rank shaped type operand
with fixes to make the test pass.
Differential Revision: https://reviews.llvm.org/D74638
Summary:
This revision adds EDSC support for VectorOps to enable the creation of a `vector_matmul` declaratively. The `vector_matmul` is a simple configuration
of the `vector.contract` op that follows the StructuredOps abstraction.
Differential Revision: https://reviews.llvm.org/D74284
