This is a minor step towards deprecating bufferization.alloc_tensor().
It replaces the examples with tensor.empty() and adjusts the underlying
rewriting logic to prepare for this upcoming change.
This commit removes the deallocation capabilities of
one-shot-bufferization. One-shot-bufferization should never deallocate
any memrefs as this should be entirely handled by the
ownership-based-buffer-deallocation pass going forward. This means the
`allow-return-allocs` pass option will default to true now,
`create-deallocs` defaults to false and they, as well as the escape
attribute indicating whether a memref escapes the current region, will
be removed. A new `allow-return-allocs-from-loops` option is added as a
temporary workaround for some bufferization limitations.
This is the first commit in a series with the goal to rework the
BufferDeallocation pass. Currently, this pass heavily relies on copies
to perform correct deallocations, which leads to very slow code and
potentially high memory usage. Additionally, there are unsupported cases
such as returning memrefs which this series of commits aims to add
support for as well.
This first commit removes the deallocation capabilities of
one-shot-bufferization.One-shot-bufferization should never deallocate any
memrefs as this should be entirely handled by the buffer-deallocation pass
going forward. This means the allow-return-allocs pass option will
default to true now, create-deallocs defaults to false and they, as well
as the escape attribute indicating whether a memref escapes the current region,
will be removed.
The documentation should w.r.t. these pass option changes should also be
updated in this commit.
Reviewed By: springerm
Differential Revision: https://reviews.llvm.org/D156662
This patch is part of a larger initiative aimed at fixing floating-point `max` and `min` operations in MLIR: https://discourse.llvm.org/t/rfc-fix-floating-point-max-and-min-operations-in-mlir/72671.
This commit addresses Task 1.2 of the mentioned RFC. By renaming these operations, we align their names with LLVM intrinsics that have corresponding semantics.
sparse_tensor ops cannot be bufferized with One-Shot Bufferize. (They can only be analyzed.) The sparse compiler does the actual lowering to memref. Produce a proper error message instead of crashing.
This fixes#61311.
Differential Revision: https://reviews.llvm.org/D158728
Replaced the "NEW_SYNTAX" with the more readable "map"
(which we may, or may not keep). Minor improvement in
keyword parsing, migrated a few more examples over.
Reviewed By: Peiming, yinying-lisa-li
Differential Revision: https://reviews.llvm.org/D158325
Consistent order of ops and related methods.
Also, renamed SpGEMMGetSizeOp to SpMatGetSizeOp
since this is a general utility for sparse matrices,
not specific to GEMM ops only.
Reviewed By: Peiming
Differential Revision: https://reviews.llvm.org/D157922
Add support for reasoning about operations with recursive memory effects
to CSE. The recursive effects are gathered by a helper function. I
decided to allow returning duplicates from the helper function because
there's no benefit to spending the computation time to remove them in
the existing use case.
Differential Revision: https://reviews.llvm.org/D156805
Improves the conversion from `DimLvlMap` to STEA, in order to correct rank-mismatch issues in the roundtrip tests.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D157162
This commit comprises a number of related changes:
(1) Reintroduces the semantic distinction between `parseVarUsage` vs `parseVarBinding`, adds documentation explaining the distinction, and adds commentary to the one place that violates the desired/intended semantics.
(2) Improves documentation/commentary about the forward-declaration of level-vars, and about the meaning of the `bool` parameter to `parseLvlSpec`.
(2) Removes the `VarEnv::addVars` method, and instead has `DimLvlMapParser` handle the conversion issues directly. In particular, the parser now stores and maintains the `{dims,lvls}AndSymbols` arrays, thereby avoiding the O(n^2) behavior of scanning through the entire `VarEnv` for each `parse{Dim,Lvl}Spec` call. Unfortunately there still remains another source of O(n^2) behavior, namely: the `AsmParser::parseAffineExpr` method will copy the `DimLvlMapParser::{dims,lvls}AndSymbols` arrays into `AffineParser::dimsAndSymbols` on each `parse{Dim,Lvl}Spec` call; but fixing that would require extensive changes to `AffineParser` itself.
Depends On D155532
Reviewed By: Peiming
Differential Revision: https://reviews.llvm.org/D155533
(1) uses the previously introduce API to reuse AffineExpr parser without codedup
(2) solves the look-ahead problem when parsing level spec
Reviewed By: Peiming
Differential Revision: https://reviews.llvm.org/D154254
This might simplify frontend implementation by avoiding recomputation for the same value.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D154244
We are in the progress of migrating to a much improved surface syntax for the Sparse Tensor Encoding Attribute (STEA).
You can see a preview of this in the StableHLO RFC at
https://github.com/openxla/stablehlo/blob/main/rfcs/20230210-sparsity.md
//**This design is courtesy Wren Romano.**//
This initial revision
(1) Introduces the first version of a new parser written by Wren Romano
(2) Introduces a simple "migration plan" using NEW_SYNTAX on the STEA, which will allow us to test the new parser with new examples, as well as migrate existing examples over without the need to rewrite them all
This first "drop" merely provides the entry points to parse the new syntax. The parser is still under active development. For example, we need to address the "lookahead" issue when parsing the lvl spec (viz. do we see l0 = d0 or a direct d0). Another larger task is to actually implement "affine" parsing (since the MLIR affine parser is not accessible in other parts of the tree).
EXAMPLE:
Currently, CSR looks like
#CSR = #sparse_tensor.encoding<{
lvlTypes = ["dense","compressed"],
dimToLvl = affine_map<(i,j) -> (i,j)>
}>
but you can "force" the new parser with
#CSR = #sparse_tensor.encoding<{
NEW_SYNTAX =
(d0, d1) -> (l0 = d0 : dense, l1 = d1 : compressed)
}>
Reviewed By: Peiming
Differential Revision: https://reviews.llvm.org/D153997
Old pattern was missing some cases (e.g. swapping the arguments)
but it also allowed too many cases (e.g. non-empty "absent" or
different arguments for add/mul). This fixes the issues.
Reviewed By: K-Wu
Differential Revision: https://reviews.llvm.org/D153487
We prevent merging a sparse-in/dense-out with dense-in
kernels because the result is usuall not sparsifiable.
Dense kernels and sparse kernels are still fused, obviously.
Reviewed By: Peiming
Differential Revision: https://reviews.llvm.org/D153077
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
Formerly, we accepted and/prod reductions as a standard
reduction but these change the semantics after sparsification
by not looking at implicit zeros. Therefore, we only accept
standard reductions that are insensitive to implicit vs.
explicit zeros, and leave the more complex reductions to
the sparse_tensor.reduce custom reduction implementation.
Reviewed By: Peiming
Differential Revision: https://reviews.llvm.org/D151929
