…ct LevelType from LevelFormat and properties instead.
**Rationale**
We used to explicitly declare every possible combination between
`LevelFormat` and `LevelProperties`, and it now becomes difficult to
scale as more properties/level formats are going to be introduced.
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.
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.
This adds COO and loose compressed to output testing. Also prepares BSR
for output testing, but needs the conversion to work first. Cleanup of
stale TODOs
Updates:
1. Infer lvlToDim from dimToLvl
2. Add more tests for block sparsity
3. Finish TODOs related to lvlToDim, including adding lvlToDim to python
binding
Verification of lvlToDim that user provides will be implemented in the
next PR.
Rationale:
This was actually just a pure "string based" test
with very little actual python usage. The output
sparse tensor was handled via the deprecated
convertFromMLIRSparseTensor method.
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
This is an ongoing series of commits that are reformatting our
Python code.
Reformatting is done with `black`.
If you end up having problems merging this commit because you
have made changes to a python file, the best way to handle that
is to run git checkout --ours <yourfile> and then reformat it
with black.
If you run into any problems, post to discourse about it and
we will try to help.
RFC Thread below:
https://discourse.llvm.org/t/rfc-document-and-standardize-python-code-style
Differential Revision: https://reviews.llvm.org/D150782
This commit is part of the migration of towards the new STEA syntax/design. In particular, this commit includes the following changes:
* Renaming compiler-internal functions/methods:
* `SparseTensorEncodingAttr::{getDimLevelType => getLvlTypes}`
* `Merger::{getDimLevelType => getLvlType}` (for consistency)
* `sparse_tensor::{getDimLevelType => buildLevelType}` (to help reduce confusion vs actual getter methods)
* Renaming external facets to match:
* the STEA parser and printer
* the C and Python bindings
* PyTACO
However, the actual renaming of the `DimLevelType` itself (along with all the "dlt" names) will be handled in a separate commit.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D150330
`PassManager.run` is currently restricted to running on `builtin.module`
ops, but this restriction doesn't exist on the C++ side. This updates it
to take `ir.Operation/OpView` instead of `ir.Module`.
Depends on D143354
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D143356
This reverts commit 5561e17411
The logic was moved from cmake into lit fixing the issue that lead to the revert and potentially others with multi-config cmake generators
Differential Revision: https://reviews.llvm.org/D143925
This patch contains the changes required to make the vast majority of integration and runner tests run on Windows.
Historically speaking, the JIT support for Windows has been lacking behind, but recent versions of ORC JIT have now caught up and works for basically all examples in repo.
Sadly due to these tests previously not working on Windows, basically all of them are making unix-like assumptions about things like filenames, paths, shell syntax etc.
This patch fixes all these issues in one big swoop and enables Windows support for the vast majority of integration tests.
More specifically, following changes had to be done:
* The various JIT runners used paths to the runtime libraries that assumed a Unix toolchain layout and filenames. I abstracted the specific path and filename of these runtime libraries away by making the paths to the runtime libraries be passed from cmake into lit. This now also allows a much more convenient syntax: `--shared-libs=%mlir_c_runner_utils` instead of `--shared-libs=%mlir_lib_dir/lib/libmlir_c_runner_utils%shlibext`
* Some tests using python set environment variables using the `ENV=VALUE cmd` format. This works on Unix, but on Windows it has to prefixed using `env ENV=VALUE cmd`
* Some tests used C functions that are simply not available or exported on Windows (`fabsf`, `aligned_alloc`). These tests have either been adjusted or explicitly marked as `UNSUPPORTED`
Some tests remain disabled on Windows as before:
* In SparseTensor some tests have non-trivial logic for finding the runtime libraries which seems to be required for the use of emulators. I do not have the time to port these so I simply kept them disabled
* Some tests requiring special hardware which I simply cannot test remain disabled on Windows. These include usage of AVX512 or AMX
The tests for `mlir-vulkan-runner` and `mlir-spirv-runner` all work now as well and so do the vast majority of `mlir-cpu-runner`.
Differential Revision: https://reviews.llvm.org/D143925
The pipeline string must now include the pass manager's anchor op. This
makes the parse API properly roundtrip the printed form of a pass
manager.
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D136405
Handle more cases of singleton DLT including direct sparse2sparse conversion. (Followup to D134096)
Depends On D134926
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D134933
This extension to the sparse tensor type system in MLIR
opens up a whole new set of sparse storage schemes, such as
block sparse storage (e.g. BCSR) and ELL (aka jagged diagonals).
This revision merely introduces the type extension and
initial documentation. The actual interpretation of the type
(reading in tensors, lowering to code, etc.) will follow.
Reviewed By: Peiming
Differential Revision: https://reviews.llvm.org/D135206
This op used to belong to the sparse dialect, but there are use cases for dense bufferization as well. (E.g., when a tensor alloc is returned from a function and should be deallocated at the call site.) This change moves the op to the bufferization dialect, which now has an `alloc_tensor` and a `dealloc_tensor` op.
Differential Revision: https://reviews.llvm.org/D129985
After https://reviews.llvm.org/D128593 this is not needed (and not available). Was missed in original landing because integration tests do not run on pre-merge.
Now that we have an AllocTensorOp (previously InitTensorOp) in the bufferization dialect, the InitOp in the sparse dialect is no longer needed.
Differential Revision: https://reviews.llvm.org/D126180
The SparseTensor passes currently use opaque numbers for the CLI, despite using an enum internally. This patch exposes the enums instead of numbered items that are matched back to the enum.
Fixes GitHub issue #53389
Reviewed by: aartbik, mehdi_amini
Differential Revision: https://reviews.llvm.org/D123876
This commit moves FuncOp out of the builtin dialect, and into the Func
dialect. This move has been planned in some capacity from the moment
we made FuncOp an operation (years ago). This commit handles the
functional aspects of the move, but various aspects are left untouched
to ease migration: func::FuncOp is re-exported into mlir to reduce
the actual API churn, the assembly format still accepts the unqualified
`func`. These temporary measures will remain for a little while to
simplify migration before being removed.
Differential Revision: https://reviews.llvm.org/D121266
The last remaining operations in the standard dialect all revolve around
FuncOp/function related constructs. This patch simply handles the initial
renaming (which by itself is already huge), but there are a large number
of cleanups unlocked/necessary afterwards:
* Removing a bunch of unnecessary dependencies on Func
* Cleaning up the From/ToStandard conversion passes
* Preparing for the move of FuncOp to the Func dialect
See the discussion at https://discourse.llvm.org/t/standard-dialect-the-final-chapter/6061
Differential Revision: https://reviews.llvm.org/D120624
These routines will need to be specialized a lot more based on value types,
index types, pointer types, and permutation/dimension ordering. This is a
careful first step, providing some functionality needed in PyTACO bridge.
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D120154
It is time to compose Linalg related optimizations with SparseTensor
related optimizations. This is a careful first start by adding some
general Linalg optimizations "upstream" of the sparse compiler in the
full sparse compiler pipeline. Some minor changes were needed to make
those optimizations aware of sparsity.
Note that after this, we will add a sparse specific fusion rule,
just to demonstrate the power of the new composition.
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D119971
The bufferization of arith.constant ops is also switched over to BufferizableOpInterface-based bufferization. The old implementation is deleted. Both implementations utilize GlobalCreator, now renamed to just `getGlobalFor`.
GlobalCreator no longer maintains a set of all created allocations to avoid duplicate allocations of the same constant. Instead, `getGlobalFor` scans the module to see if there is already a global allocation with the same constant value.
For compatibility reasons, it is still possible to create a pass that bufferizes only `arith.constant`. This pass (createConstantBufferizePass) could be deleted once all users were switched over to One-Shot bufferization.
Differential Revision: https://reviews.llvm.org/D118483
explores various sparsity combinations of
the SDMM kernel and verifies that the computed
result is the same for all cases
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D115476
