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See https://github.com/llvm/llvm-project/pull/147168 for more info.
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This is a code cleanup. Update a few places in MLIR that should use
`hasSingleElement`/`getSingleElement`.
Note: `hasSingleElement` is faster than `.getSize() == 1` when it is
used with linked lists etc.
Depends on #131508.
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loop (#95858)
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This ensures the explicit value is generated (and not a load into the
values array). Note that actually not storing values array at all is
still TBD, this is just the very first step.
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(#84987)
…a sparse tensor.
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1. Add parsing methods for block[n, m].
2. Encode n and m with the newly extended 64-bit LevelType enum.
3. Update 2:4 methods names/comments to n:m.
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sparse tensor level. (#78345)
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This commit renames 4 pattern rewriter API functions:
* `updateRootInPlace` -> `modifyOpInPlace`
* `startRootUpdate` -> `startOpModification`
* `finalizeRootUpdate` -> `finalizeOpModification`
* `cancelRootUpdate` -> `cancelOpModification`
The term "root" is a misnomer. The root is the op that a rewrite pattern
matches against
(https://mlir.llvm.org/docs/PatternRewriter/#root-operation-name-optional).
A rewriter must be notified of all in-place op modifications, not just
in-place modifications of the root
(https://mlir.llvm.org/docs/PatternRewriter/#pattern-rewriter). The old
function names were confusing and have contributed to various broken
rewrite patterns.
Note: The new function names use the term "modify" instead of "update"
for consistency with the `RewriterBase::Listener` terminology
(`notifyOperationModified`).
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`hasTensor/BufferSemantics` (#77574)
Rename interface functions as follows:
* `hasTensorSemantics` -> `hasPureTensorSemantics`
* `hasBufferSemantics` -> `hasPureBufferSemantics`
These two functions return "true" if the op has tensor/buffer operands
but not buffer/tensor operands.
Also drop the "ranked" part from the interface, i.e., do not distinguish
between ranked/unranked types.
The new function names describe the functions more accurately. They also
align their semantics with the notion of "tensor semantics" with the
bufferization framework. (An op is supposed to be bufferized if it has
tensor operands, and we don't care if it also has memref operands.)
This change is in preparation of #75273, which adds
`BufferizableOpInterface::hasTensorSemantics`. By renaming the functions
in the `DestinationStyleOpInterface`, we can avoid name clashes between
the two interfaces.
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(#76764)
…et in a lattice point.
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Separates actual transformation files from supporting utility files in
the transforms directory. Includes a bazel overlay fix for the build (as
well as a bit of cleanup of that file to be less verbose and more
flexible).
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Bug found with BSR of "spy" SDDMM method
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Removes at in favor of curr; also makes method delegates consistent
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This adds a consistent usage with `at` for everything that refers to the
current loop nesting. This cleans up some redundant legacy code from
when we were still using topSort inside sparsifier code.
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Rationale:
We no longer deal with topsort during sparsification, so that LoopId ==
LoopOrd for all methods. This first revision removes the types. A follow
up revision will simplify some other remaining constructs that deal with
loop order (e.g. at and ldx).
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Removed obsoleted TODOs and NOTEs, formatting, removed unused parameter
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(#73597)
… loop bound.
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The "Dim" prefix is a legacy left-over that no longer makes sense, since
we have a very strict "Dimension" vs. "Level" definition for sparse
tensor types and their storage.
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The "dimension" before "level" does not really make sense Note that
renaming the actual type DimLevelType to LevelType is still TBD, since
this is an externally visible change (e.g. visible to Python API).
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sparsification. (#72423)
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detail see the docment: https://mlir.llvm.org/deprecation/
Not all changes are made manually, most of them are made through a clang
tool I wrote https://github.com/lipracer/cpp-refactor.
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(#71840)
…ffine subscript expressions.
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When the Powers That Be decided that the name "sparse compiler" should
be changed to "sparsifier", we negected to change some of the comments
in the code; this pull request completes the name change.
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This test used to be here, but somehow got lost while linalg rewrote
their interfaces. It is essential to test this on entry of
sparsification, however, since all subsequent analysis simply assumes
tensor types.
Fixes:
https://github.com/llvm/llvm-project/issues/64325
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Change CompressedWithHigh to LooseCompressed.
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**Dense**
`lvlTypes = [ "dense", "dense" ]` to `map = (d0, d1) -> (d0 : dense, d1
: dense)`
`lvlTypes = [ "dense", "dense" ], dimToLvl = affine_map<(i,j) -> (j,i)>`
to `map = (d0, d1) -> (d1 : dense, d0 : dense)`
**DCSR**
`lvlTypes = [ "compressed", "compressed" ]` to `map = (d0, d1) -> (d0 :
compressed, d1 : compressed)`
**DCSC**
`lvlTypes = [ "compressed", "compressed" ], dimToLvl = affine_map<(i,j)
-> (j,i)>` to `map = (d0, d1) -> (d1 : compressed, d0 : compressed)`
**Block Row**
`lvlTypes = [ "compressed", "dense" ]` to `map = (d0, d1) -> (d0 :
compressed, d1 : dense)`
**Block Column**
`lvlTypes = [ "compressed", "dense" ], dimToLvl = affine_map<(i,j) ->
(j,i)>` to `map = (d0, d1) -> (d1 : compressed, d0 : dense)`
This is an ongoing effort: #66146, #66309
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to LoopEmitter.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D158914
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Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D156941
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Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D152601
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Reviewed By: aartbik, K-Wu
Differential Revision: https://reviews.llvm.org/D153397
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This specific operation matches the cuSPARSE SDDMM semantics exactly.
Reviewed By: Peiming
Differential Revision: https://reviews.llvm.org/D152969
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operation
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D152761
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not be found
Reviewed By: aartbik, K-Wu
Differential Revision: https://reviews.llvm.org/D152877
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enterCoIterationOverTensorsAtLvls
The tensor levels are now explicitly categorized into different `LoopCondKind` to instruct LoopEmitter generate different code for different kinds of condition (e.g., `SparseCond`, `SparseSliceCond`, `SparseAffineIdxCond`, etc)
The process of generating a while loop is now dissembled into three steps and they are dispatched to different LoopCondKind handler.
1. Generate LoopCondition (e.g., `pos <= posHi` for `SparseCond`, `slice.isNonEmpty` for `SparseAffineIdxCond`)
2. Generate LoopBody (e.g., compute the coordinates)
3. Generate ExtraChecks (e.g., `if (onSlice(crd))` for `SparseSliceCond`)
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D152464
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Document better that unary/binary may only feed to the output
or the input of a custom reduction (not even a regular reduction
since it may have "no value"!). Also fixes a bug when present
branch is empty and feeds into custom reduction.
Reviewed By: Peiming
Differential Revision: https://reviews.llvm.org/D152224
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Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D152048
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Note that by sparse compiler convention, dense output
is zerod out when not set, so complement results in
zeros where elements were present.
Reviewed By: wrengr
Differential Revision: https://reviews.llvm.org/D152046
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Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D151776
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