`getDescriptorFromTensorTuple` and `getMutDescriptorFromTensorTuple`
extract the tensor type from an `unrealized_conversion_cast` op that
serves as a workaround for missing 1:N dialect conversion support.
This commit changes these functions so that they explicitly receive the
tensor type as a function argument. This is in preparation of merging
the 1:1 and 1:N conversion drivers. The conversion patterns in this file
will soon start receiving multiple SSA values (`ValueRange`) from their
adaptors (instead of a single value that is the result of
`unrealized_conversion_cast`). It will no longer be possible to take the
tensor type from the `unrealized_conversion_cast` op. The
`unrealized_conversion_cast` workaround will disappear entirely.
This commit adds a new function
`ConversionPatternRewriter::replaceOpWithMultiple`. This function is
similar to `replaceOp`, but it accepts multiple `ValueRange`
replacements, one per op result.
Note: This function is not an overload of `replaceOp` because of
ambiguous overload resolution that would make the API difficult to use.
This commit aligns "block signature conversions" with "op replacements":
both support 1:N replacements now. Due to incomplete 1:N support in the
dialect conversion driver, an argument materialization is inserted when
an SSA value is replaced with multiple values; same as block signature
conversions already work around the problem. These argument
materializations are going to be removed in a subsequent commit that
adds full 1:N support. The purpose of this PR is to add missing features
gradually in small increments.
This commit also updates two MLIR transformations that have their custom
workarounds around missing 1:N support. These can already start using
`replaceOpWithMultiple`.
Co-authored-by: Markus Böck <markus.boeck02@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.
Codegen "vectors" for pos/crd/val use the capacity as memref size, not
the actual used size. Although the sparsifier itself always uses just
the defined pos/crd/val parts, printing these and passing them back to a
runtime environment could benefit from wrapping the basic pos/crd/val
getters into a proper memref view that sets the right size.
This commit adds a new test-only op:
`sparse_tensor.has_runtime_library`. The op returns "1" if the sparse
compiler runs in runtime library mode.
This op is useful for writing test cases that require different IR
depending on whether the sparse compiler runs in runtime library or
codegen mode.
This commit fixes a memory leak in `sparse_pack_d.mlir`. This test case
uses `sparse_tensor.assemble` to create a sparse tensor SSA value from
existing buffers. This runtime library reallocates+copies the existing
buffers; the codegen path does not. Therefore, the test requires
additional deallocations when running in runtime library mode.
Alternatives considered:
- Make the codegen path allocate. "Codegen" is the "default" compilation
mode and it is handling `sparse_tensor.assemble` correctly. The issue is
with the runtime library path, which should not allocate. Therefore, it
is better to put a workaround in the runtime library path than to work
around the issue with a new flag in the codegen path.
- Add a `sparse_tensor.runtime_only` attribute to
`bufferization.dealloc_tensor`. Verifying that the attribute can only be
attached to `bufferization.dealloc_tensor` may introduce an unwanted
dependency of `MLIRSparseTensorDialect` on `MLIRBufferizationDialect`.
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).
This centralizes all COO methods, and provides a cleaner API. Note that
the "enc" only constructor is a temporary workaround the need for COO
methods inside the "enc" only storage specifier.
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.
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).
This change implements the correct *level* sizes set up for the direct
IR codegen fields in the sparse storage scheme. This brings libgen and
codegen together again.
This is step 3 out of 3 to make sparse_tensor.new work for BSR
This change provides access to the individual components of dim sizes
and lvl sizes after each codegenutil call.
This is step 2 out of 3 to make sparse_tensor.new work for BSR
Note that the (dis)assemble operations still make some simplfying
assumptions (e.g. trailing 2-D COO in AoS format) but now at least both
the direct IR and support library path behave exactly the same.
Generalizing the ops is still TBD.
This is a first revision in a small series of changes that removes
duplications between direct encoding methods and sparse tensor type
wrapper methods (in favor of the latter abstraction, since it provides
more safety). The goal is to simply end up with "just" SparseTensorType
This revision introduces a MapRef, which will support a future
generalization beyond permutations (e.g. block sparsity). This revision
also unifies the conversion/codegen paths for the sparse_tensor.new
operation from file (eg. the readers). Note that more unification is
planned as well as general affine dim2lvl and lvl2dim (all marked with
TODOs).
This might simplify frontend implementation by avoiding recomputation for the same value.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D154244
(These factories are used in downstream code, despite not being used within the MLIR codebase.)
Depends On D151513
Reviewed By: Peiming
Differential Revision: https://reviews.llvm.org/D151518
This is a major step along the way towards the new STEA design. While a great deal of this patch is simple renaming, there are several significant changes as well. I've done my best to ensure that this patch retains the previous behavior and error-conditions, even though those are at odds with the eventual intended semantics of the `dimToLvl` mapping. Since the majority of the compiler does not yet support non-permutations, I've also added explicit assertions in places that previously had implicitly assumed it was dealing with permutations.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D151505
The MLIR classes Type/Attribute/Operation/Op/Value support
cast/dyn_cast/isa/dyn_cast_or_null functionality through llvm's doCast
functionality in addition to defining methods with the same name.
This change begins the migration of uses of the method to the
corresponding function call as has been decided as more consistent.
Note that there still exist classes that only define methods directly,
such as AffineExpr, and this does not include work currently to support
a functional cast/isa call.
Context:
- https://mlir.llvm.org/deprecation/ at "Use the free function variants
for dyn_cast/cast/isa/…"
- Original discussion at https://discourse.llvm.org/t/preferred-casting-style-going-forward/68443
Implementation:
This patch updates all remaining uses of the deprecated functionality in
mlir/. This was done with clang-tidy as described below and further
modifications to GPUBase.td and OpenMPOpsInterfaces.td.
Steps are described per line, as comments are removed by git:
0. Retrieve the change from the following to build clang-tidy with an
additional check:
main...tpopp:llvm-project:tidy-cast-check
1. Build clang-tidy
2. Run clang-tidy over your entire codebase while disabling all checks
and enabling the one relevant one. Run on all header files also.
3. Delete .inc files that were also modified, so the next build rebuilds
them to a pure state.
```
ninja -C $BUILD_DIR clang-tidy
run-clang-tidy -clang-tidy-binary=$BUILD_DIR/bin/clang-tidy -checks='-*,misc-cast-functions'\
-header-filter=mlir/ mlir/* -fix
rm -rf $BUILD_DIR/tools/mlir/**/*.inc
```
Differential Revision: https://reviews.llvm.org/D151542
