In `Gather1DToConditionalLoads`, currently we will check if the stride
of the most minor dim of the input memref is 1. And if not, the
rewriting pattern will not be applied. However, according to the
verification of `vector.load` here:
4e32271e8b/mlir/lib/Dialect/Vector/IR/VectorOps.cpp (L4971-L4975)
.. if the output vector type of `vector.load` contains only one element,
we can ignore the requirement of the stride of the input memref, i.e.
the input memref can be with any stride layout attribute in such case.
So here we can allow more cases in lowering `vector.gather` by relaxing
such check.
As shown in the test case attached in this patch
[here](1933fbad58/mlir/test/Dialect/Vector/vector-gather-lowering.mlir (L151)),
now `vector.gather` of memref with non-trivial stride can be lowered
successfully if the result vector contains only one element.
---------
Signed-off-by: PragmaTwice <twice@apache.org>
Co-authored-by: Andrzej Warzyński <andrzej.warzynski@gmail.com>
This commit updates the internal `ConversionValueMapping` data structure
in the dialect conversion driver to support 1:N replacements. This is
the last major commit for adding 1:N support to the dialect conversion
driver.
Since #116470, the infrastructure already supports 1:N replacements. But
the `ConversionValueMapping` still stored 1:1 value mappings. To that
end, the driver inserted temporary argument materializations (converting
N SSA values into 1 value). This is no longer the case. Argument
materializations are now entirely gone. (They will be deleted from the
type converter after some time, when we delete the old 1:N dialect
conversion driver.)
Note for LLVM integration: Replace all occurrences of
`addArgumentMaterialization` (except for 1:N dialect conversion passes)
with `addSourceMaterialization`.
---------
Co-authored-by: Markus Böck <markus.boeck02@gmail.com>
Vector::BroadCastOp expects the identical element type in folding. It
causes the crash if the different source type is given to the SCCP pass.
We need to guard the pass from crashing if the nonidentical element type
is given, but still compatible. (e.g. index vs integer type)
https://github.com/llvm/llvm-project/issues/120193
The greedy rewriter is used in many different flows and it has a lot of
convenience (work list management, debugging actions, tracing, etc). But
it combines two kinds of greedy behavior 1) how ops are matched, 2)
folding wherever it can.
These are independent forms of greedy and leads to inefficiency. E.g.,
cases where one need to create different phases in lowering and is
required to applying patterns in specific order split across different
passes. Using the driver one ends up needlessly retrying folding/having
multiple rounds of folding attempts, where one final run would have
sufficed.
Of course folks can locally avoid this behavior by just building their
own, but this is also a common requested feature that folks keep on
working around locally in suboptimal ways.
For downstream users, there should be no behavioral change. Updating
from the deprecated should just be a find and replace (e.g., `find ./
-type f -exec sed -i
's|applyPatternsAndFoldGreedily|applyPatternsGreedily|g' {} \;` variety)
as the API arguments hasn't changed between the two.
Note that PointerUnion::{is,get} have been soft deprecated in
PointerUnion.h:
// FIXME: Replace the uses of is(), get() and dyn_cast() with
// isa<T>, cast<T> and the llvm::dyn_cast<T>
I'm not touching PointerUnion::dyn_cast for now because it's a bit
complicated; we could blindly migrate it to dyn_cast_if_present, but
we should probably use dyn_cast when the operand is known to be
non-null.
Continue the move of `warp_execute_on_lane_0` op to the gpu dialect
(#116994). This patch creates a utils library in GPU and moves generic
helper functions there.
The extract <-> shape cast folder was conservatively asserting and
failing on 0-d vectors. This pr fixes this.
This pr also adds more tests for 0d cases and updates related tests to
better reflect what they test.
This patch simplifies and extends the logic used when compressing masks
emitted by `vector.constant_mask` to support extracting 1-D vectors from
multi-dimensional vector loads. It streamlines mask computation, making
it applicable for multi-dimensional mask generation, improving the
overall handling of masked load operations.
When inferring the mask of a transfer operation that results in a single `i1` element,
we could represent it using either `vector<i1>` or vector<1xi1>. To avoid type mismatches,
this PR updates the mask inference logic to consistently generate `vector<1xi1>` for
these cases. We can enable 0-D masks if they are needed in the future.
See: https://github.com/llvm/llvm-project/issues/116197
Previously when `numFrontPadElems` is not zero, `getCompressedMaskOp`
produces wrong result if the mask generator op is a
`vector.create_mask`.
This patch resolves the issue by including `numFrontPadElems` into the
mask generation.
Signed-off-by: Alan Li <me@alanli.org>
Previously the patch was not expecting to handle non-static index, when
the index is a non constant value it will crash.
This patch is to make sure it return gracefully instead of crashing.
This is a NFC-ish change that moves
vector.extractelement/vector.insertelement vector distribution patterns
to vector.insert/vector.extract.
Before:
0-d/1-d vector.extract -> vector.extractelement -> distributed
vector.extractelement
2-d+ vector.extract -> distributed vector.extract
After:
scalar input vector.extract -> distributed vector.extract
vector.extractelement -> distributed vector.extract
2d+ vector.extract -> distributed vector.extract
The same changes are done for insertelement/insert. The change allows us
to remove reliance on vector.extractelement/vector.insertelement, which
are soon to be depreciated:
https://discourse.llvm.org/t/rfc-psa-remove-vector-extractelement-and-vector-insertelement-ops-in-favor-of-vector-extract-and-vector-insert-ops/71116/8
No extra tests are included because this patch doesn't introduce /
remove any functionality. It only changes the chain of lowerings. This
change can be completly NFC if we make the distributed operation
vector.extractelement/vector.insertelement, but that is slightly weird,
because you are going from extractelement -> extract -> extractelement.
This commit adds support for handling mask constants generated by the
`arith.constant` op in the `VectorEmulateNarrowType` pattern.
Previously, this pattern would not match due to the lack of mask
constant handling in `getCompressedMaskOp`.
The changes include:
1. Updating `getCompressedMaskOp` to recognize and handle
`arith.constant` ops as mask value sources.
2. Handling cases where the mask is not aligned with the emulated load
width. The compressed mask is adjusted to account for the offset.
Limitations:
- The arith.constant op can only have 1-dimensional constant values.
Resolves: #115742
Signed-off-by: Alan Li <me@alanli.org>
Add a new helper function `isReachable` to `Block`. This function
traverses all successors of a block to determine if another block is
reachable from the current block.
This functionality has been reimplemented in multiple places in MLIR.
Possibly additional copies in downstream projects. Therefore, moving it
to a common place.
In `staticallyExtractSubvector`, When the extracting slice is the same
as source vector, do not need to emit `vector.extract_strided_slice`.
This fixes the lit test case `@vector_store_i4` in
`mlir\test\Dialect\Vector\vector-emulate-narrow-type.mlir`, where
converting from `vector<8xi4>` to `vector<4xi8>` does not need slice
extraction.
The issue was introduced in #113411 and #115070, CI failure link:
https://buildkite.com/llvm-project/github-pull-requests/builds/118845
This PR does not include a lit test case because it is a fix and the
above mentioned `@vector_store_i4` test actually tests the mechanism.
Signed-off-by: Alan Li <me@alanli.org>
All patterns in populateVectorNarrowTypeEmulationPatterns currently
assume a 1-D vector load/store rather than an n-D vector load/store.
This assumption is evident in ConvertVectorTransferRead, for example,
here (extracted from `ConvertVectorTransferRead`):
```cpp
auto newRead = rewriter.create<vector::TransferReadOp>(
loc, VectorType::get(numElements, newElementType), adaptor.getSource(),
getValueOrCreateConstantIndexOp(rewriter, loc, linearizedIndices),
newPadding);
auto bitCast = rewriter.create<vector::BitCastOp>(
loc, VectorType::get(numElements * scale, oldElementType), newRead);
```
Both invocations of `VectorType::get()` here generate a 1-D vector.
Attempts to use these patterns with more generic cases, such as 2-D
vectors, fail. For example, trying to cast the following 2-D case to
`i32`:
```mlir
func.func @vector_maskedload_2d_i8_negative(
%idx1: index,
%idx2: index,
%num_elems: index,
%passthru: vector<2x4xi8>) -> vector<2x4xi8> {
%0 = memref.alloc() : memref<3x4xi8>
%mask = vector.create_mask %num_elems, %num_elems : vector<2x4xi1>
%1 = vector.maskedload %0[%idx1, %idx2], %mask, %passthru :
memref<3x4xi8>, vector<2x4xi1>, vector<2x4xi8> into vector<2x4xi8>
return %1 : vector<2x4xi8>
}
```
For example, casting to i32 produces:
```bash
error: 'vector.bitcast' op failed to verify that all of {source, result} have same rank
%1 = vector.maskedload %0[%idx1, %idx2], %mask, %passthru :
^
```
Instead of reworking these patterns (that's going to require much more
effort), I’ve marked them as 1-D only and extended
"TestEmulateNarrowTypePass" with an option to disable the Memref type
converter - that's to be able to add negative tests (otherwise, the type
converter throws an error we can't really test for). While not ideal,
this workaround should suit a test pass.
Based on existing emulating scheme, this patch expands to support
dynamic indexing by dynamically create intermediate new mask, new pass
thru vector and dynamically insert the result into destination vector.
the dynamic parts are constructed by multiple `vector.extract` and
`vector.insert` to rearrange the original mask/passthru vector, as
`vector.insert_strided_slice` and `vector.extract_strided_slice` only
take static offsets and indices.
Note: currently only supporting `vector.maskedload` with masks created
by `vector.constant_mask`. `vector.create_mask` is currently not
working.
---------
Co-authored-by: hasekawa-takumi <167335845+hasekawa-takumi@users.noreply.github.com>
The documentation for narrow-type emulation was sparse, so I’ve expanded
it with additional clarifications (e.g., specifying that the example
discusses `i4` -> `i8` emulation).
I also noticed some inconsistencies in testing for narrow-type
emulation, with several cases covered only for "loading" and missing for
"storing." To address this, I’ve:
* Added comments in the test file for easier reference,
* Added the missing tests for `vector.maskedstore`.
Additionally, I’ve renamed tests for `vector.masked{load|store}` for
clarity:
* `@vector_cst_maskedload_i8` -> `@vector_maskedload_i8_constant_mask`.
This makes it easier to contrast with similar functions, such as
`@vector_maskedload_i8`.
Lastly, I’ve added a high-level comment in VectorEmulateNarrowType.cpp
to clarify the overall design and intent of the file.
This PR adds a folder for extracting an element from a vector shuffle.
It turns something like:
```
%shuffle = vector.shuffle %a, %b [0, 8, 7, 15]
: vector<8xf32>, vector<8xf32>
%extract = vector.extract %shuffle[3] : f32 from vector<4xf32>
```
into:
```
%extract = vector.extract %b[7] : f32 from vector<8xf32>
```
This patch fixes:
mlir/lib/Dialect/Vector/Transforms/VectorEmulateNarrowType.cpp:202:2:
error: extra ';' outside of a function is incompatible with C++98
[-Werror,-Wc++98-compat-extra-semi]
* Supports `vector.load` and `vector.transfer_read` ops.
* In the case of dynamic indexing, use per-element insertion/extraction
to build desired narrow type vectors.
* Fixed wrong function comment of `getCompressedMaskOp`.
---------
Co-authored-by: Han-Chung Wang <hanhan0912@gmail.com>
Treat integer range for vector type as union of ranges of individual
elements. With this semantics, most arith ops on vectors will work out
of the box, the only special handling needed for constants and vector
elements manipulation ops.
The end goal of these changes is to be able to optimize vectorized index
calculations.
Currently, the lowering for vector.step lives
under a folder. This is not ideal if we want
to do transformation on it and defer the
materizaliztion of the constants much later.
This commits adds a rewrite pattern that
could be used by using
`transform.structured.vectorize_children_and_apply_patterns`
transform dialect operation.
Moreover, the rewriter of vector.step is also
now used in -convert-vector-to-llvm pass where
it handles scalable and non-scalable types as
LLVM expects it.
As a consequence of removing the vector.step
lowering as its folder, linalg vectorization
will keep vector.step intact.
Previously, the pass only supported emulation of loading vector sizes
that are multiples of the emulated data type. This patch expands its
support for emulating sizes that are not multiples of byte sizes. In
such cases, the element values are packed back-to-back to preserve
memory space.
To give a concrete example: if an input has type `memref<3x3xi2>`, it is
actually occupying 3 bytes in memory, with the first 18 bits storing the
values and the last 6 bits as padding. The slice of `vector<3xi2>` at
index `[2, 0]` is stored in memory from bit 12 to bit 18. To properly
load the elements from bit 12 to bit 18 from memory, first load byte 2
and byte 3, and convert it to a vector of `i2` type; then extract bits 4
to 10 (element index 2-5) to form a `vector<3xi2>`.
A limitation of this patch is that the linearized index of the unaligned
vector has to be known at compile time. Extra code needs to be emitted
to handle it if the condition does not hold.
The following ops are updated:
* `vector::LoadOp`
* `vector::TransferReadOp`
* `vector::MaskedLoadOp`
The current vector.insert folder tries to replace a scalar with a 0-rank
vector. This patch fixes this crash by not folding unless they types of
the result and replacement are same.
Since
ddf2d62c7d
, 0-d vectors are supported in VectorType. This patch removes 0-d vector
handling with scalars for the TransferOpReduceRank pattern. This pattern
specifically introduces tensor.extract_slice during vectorization,
causing vectorization to not fold transfer_read/transfer_write slices
properly. The changes in vectorization test files reflect this.
There are other places where lowering patterns are still side-stepping
from handling 0-d vectors properly, by turning them into scalars, but
this patch only focuses on the vector.transfer_x patterns.
This is a reasonable canonicalization because `extract` is more
constrained than `extract_strided_slices`, so there is no loss of
semantics here, just lifting an op to a special-case higher/constrained
op. And the additional `shape_cast` is merely adding leading unit dims
to match the original result type.
Context: discussion on #111541. I wasn't sure how this would turn out,
but in the process of writing this PR, I discovered at least 2 bugs in
the pattern introduced in #111541, which shows the value of shared
canonicalization patterns which are exercised on a high number of
testcases.
---------
Signed-off-by: Benoit Jacob <jacob.benoit.1@gmail.com>
This commit marks the type converter in `populate...` functions as
`const`. This is useful for debugging.
Patterns already take a `const` type converter. However, some
`populate...` functions do not only add new patterns, but also add
additional type conversion rules. That makes it difficult to find the
place where a type conversion was added in the code base. With this
change, all `populate...` functions that only populate pattern now have
a `const` type converter. Programmers can then conclude from the
function signature that these functions do not register any new type
conversion rules.
Also some minor cleanups around the 1:N dialect conversion
infrastructure, which did not always pass the type converter as a
`const` object internally.
NOTE: This is a follow-up for #97049 in which the `in_bounds` attribute
was made mandatory.
This PR updates the semantics of the `in_bounds` attribute so that
broadcast dimensions are no longer required to be "in bounds".
Specifically, these xfer_read/xfer_write Ops become valid after this
change:
```mlir
%read = vector.transfer_read %A[%base1, %base2], %pad
{in_bounds = [false], permutation_map = affine_map<(d0, d1) -> (0)>}
{permutation_map = affine_map<(d0, d1) -> (0)>}
: memref<?x?xf32>, vector<9xf32>
vector.transfer_write %vec, %A[%base1, %base2],
{in_bounds = [false], permutation_map = affine_map<(d0, d1) -> (0)>}
{permutation_map = affine_map<(d0, d1) -> (0)>}
: vector<9xf32>, memref<?x?xf32>
```
Note that the value `false` merely means "may run out-of-bounds", i.e.,
the corresponding access can still be "in bounds". In fact, the folder
for xfer Ops is also updated (*) and will update the attribute value
corresponding to broadcast dims to `true` if all non-broadcast dims
are marked as "in bounds".
Note that this PR doesn't change any of the lowerings. The changes in
"SuperVectorize.cpp", "Vectorization.cpp" and "AffineMap.cpp" are simple
reverts of recent changes in #97049. Those were only meant to facilitate
making `in_bounds` mandatory and to work around the extra requirements
for broadcast dims (those requirements ere removed in this PR). All
changes in tests are also reverts of changes from #97049.
For context, here's a PR in which "broadcast" dims where forced to
always be "in-bounds":
* https://reviews.llvm.org/D102566
(*) See `foldTransferInBoundsAttribute`.
`vector.transfer_*` folding and forwarding currently does not take into
account reshaping view-like memref ops (expand and collapse shape),
leading to potentially invalid store folding or value forwarding. This
patch adds tracking for those (and other) view-like ops. It is still
possible to design operations that alias memrefs without being a view
(e.g. memref in the iter_args of an `scf.for`), so these patterns may
still need revisiting in the future.
Adds a new Transform Dialect Op that collects patters for dropping unit
dims from various Ops:
* `transform.apply_patterns.vector.drop_unit_dims_with_shape_cast`.
It excludes patterns for vector.transfer Ops - these are collected
under:
* `apply_patterns.vector.rank_reducing_subview_patterns`,
and use ShapeCastOp _and_ SubviewOp to reduce the rank (and to eliminate
unit dims).
This new TD Ops allows us to test the "ShapeCast folder" pattern in
isolation. I've extracted the only test that I could find for that
folder from "vector-transforms.mlir" and moved it to a dedicated file:
"shape-cast-folder.mlir". I also added a test case with scalable
vectors.
Changes in VectorTransforms.cpp are not needed (added a comment with
a TODO + ordered the patterns alphabetically). I am Including them here
to avoid a separate PR.
There are some spurious libraries which can be removed.
I'm trying to bundle MLIR/LLVM library dependencies for our own
libraries. We're utilizing cmake function to recursively collect
MLIR/LLVM related dependencies. However, we identified certain library
dependencies as redundant and safe for removal.
This adds a pattern for dropping unit dims from the iter_args of scf.for
ops using vector.shape_cast. This composes with the other patterns for
dropping unit dims from elementwise ops and transposes.
