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
The logic enabling the Arm SVE (and now SME) integration tests for
various dialects, that may run under emulation, is now duplicated in
several places.
This patch moves the configuration to the top-level MLIR integration
tests Lit config and renames the '%lli' substitution in contexts where
it will run exclusively (ArmSVE, ArmSME) on AArch64 (and possibly under
emulation) to '%lli_aarch64_cmd', and '%lli_host_or_aarch64_cmd' for
contexts where it may run AArch64 (also possibly under emulation). The
latter is for integration tests that have target-specific and
target-agnostic codepaths such as SparseTensor, which supports scalable
vectors.
The two substitutions have the same effect but the names are different to
convey this information. The '%lli_aarch64_cmd' substitution could be
used in the SparseTensor tests but that would be a misnomer if the host
were x86 and the MLIR_RUN_SVE_TESTS=OFF.
The reason for renaming the '%lli' substitution is to not prevent running other
target-specific integration tests at the same time, since the same substitution
'%lli' is used for lli in other integration tests:
* mlir/test/Integration/Dialect/Vector/CPU/X86Vector - (AVX emulation via Intel SDE)
* mlir/test/Integration/Dialect/Vector/CPU/AMX - (AMX emulation via Intel SDE)
* mlir/test/Integration/Dialect/LLVMIR/CPU/test-vp-intrinsic.mlir - (RISCV emulation via QEMU if supported, native otherwise)
and substituting '%lli' at the top-level with Arm specific logic would override
this.
Reviewed By: awarzynski
Differential Revision: https://reviews.llvm.org/D148929
This reverts commit 5561e174117ff395d65b6978d04b62c1a1275138
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
This patch updates the remaining SparseCompiler integration tests to
target SVE when available.
Two tests will require some investigation in the future:
* sparse_matmul.mlir
* sparse_tanh.mlir
The former passes regardless - that's due to how `CHECK` lines are
defined. The latter fails when SVE is enabled, but passes when it's
disabled. I marked it as UNSUPPORTED as there is no mechanism to XFAIL a
test conditionally. Also, see [1] for more details.
[1] https://github.com/llvm/llvm-project/issues/60626
Differential Revision: https://reviews.llvm.org/D143514
This is generated by running
```
sed --in-place 's/[[:space:]]\+$//' mlir/**/*.td
sed --in-place 's/[[:space:]]\+$//' mlir/**/*.mlir
```
Reviewed By: rriddle, dcaballe
Differential Revision: https://reviews.llvm.org/D138866
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
This change removes the partial bufferization passes from the sparse compilation pipeline and replaces them with One-Shot Bufferize. One-Shot Analysis (and TensorCopyInsertion) is used to resolve all out-of-place bufferizations, dense and sparse. Dense ops are then bufferized with BufferizableOpInterface. Sparse ops are still bufferized in the Sparsification pass.
Details:
* Dense allocations are automatically deallocated, unless they are yielded from a block. (In that case the alloc would leak.) All test cases are modified accordingly. E.g., some funcs now have an "out" tensor argument that is returned from the function. (That way, the allocation happens at the call site.)
* Sparse allocations are *not* automatically deallocated. They must be "released" manually. (No change, this will be addressed in a future change.)
* Sparse tensor copies are not supported yet. (Future change)
* Sparsification no longer has to consider inplacability. If necessary, allocations and/or copies are inserted during TensorCopyInsertion. All tensors are inplaceable by the time Sparsification is running. Instead of marking a tensor as "not inplaceable", it can be marked as "not writable", which will trigger an allocation and/or copy during TensorCopyInsertion.
Differential Revision: https://reviews.llvm.org/D129356
Adding more test cases for sparse_tensor.BinaryOp, including different cases when overlap/left/right region is implemented/empty/identity
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D128383
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
This was leftover from when the standard dialect was destroyed, and
when FuncOp moved to the func dialect. Now that these transitions
have settled a bit we can drop these.
Most updates were handled using a simple regex: replace `^( *)func` with `$1func.func`
Differential Revision: https://reviews.llvm.org/D124146
Adding lowering for Unary and Binary required several changes due to
their unique nature of containing custom code for different "regions"
of the sparse structure being operated on. Along with a Kind, a pointer
to the Operation is passed along to be merged once the lattice
structure is figured out.
The original operation is maintained, as it is required for subsequent
lattice decisions. However, sparse_tensor.binary has some branches
are considered as fully handled and therefore are marked with as
kBinaryBranch to distinguish them.
A unique aspect of the custom code is that sometimes the desired result
is no result at all -- i.e. a user wants overlapping sparse entries to
become empty in the output. The solution to this is to return an
uninitialized Value(), which is checked and handled elsewhere in the
code and results in nothing being written to the output tensor for that
case.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D123057
