When only all-dense "sparse" tensors occur in a function prototype, the
assembler would skip the method conversion purely based on input/output
counts. It should rewrite based on the presence of any annotation,
however.
To run integration tests using qemu-aarch64 on x64 host, below flags are
added to the cmake command when building mlir/llvm:
-DMLIR_INCLUDE_INTEGRATION_TESTS=ON \
-DMLIR_RUN_ARM_SVE_TESTS=ON \
-DMLIR_RUN_ARM_SME_TESTS=ON \
-DARM_EMULATOR_EXECUTABLE="<...>/qemu-aarch64" \
-DARM_EMULATOR_OPTIONS="-L /usr/aarch64-linux-gnu" \
-DARM_EMULATOR_MLIR_CPU_RUNNER_EXECUTABLE="<llvm_arm64_build_top>/bin/mlir-cpu-runner-arm64"
\
-DARM_EMULATOR_LLI_EXECUTABLE="<llvm_arm64_build_top>/bin/lli" \
-DARM_EMULATOR_UTILS_LIB_DIR="<llvm_arm64_build_top>/lib"
The last three above are prebuilt on, or cross-built for, an aarch64
host.
This patch introduced substittutions of "%native_mlir_runner_utils" etc. and use
them in SVE/SME integration tests. When configured to run using qemu-aarch64,
mlir runtime util libs will be loaded from ARM_EMULATOR_UTILS_LIB_DIR, if set.
Some tests marked with 'UNSUPPORTED: target=aarch64{{.*}}' are still run
when configured with ARM_EMULATOR_EXECUTABLE and the default target is
not aarch64.
A lit config feature 'mlir_arm_emulator' is added in
mlir/test/lit.site.cfg.py.in and to UNSUPPORTED list of such tests.
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 fixes the following error when stopping the sparse compiler
pipeline after bufferization (e.g., with `test-analysis-only`):
```
LLVM ERROR: Building op `vector.print` but it isn't known in this MLIRContext: the dialect may not be loaded or this operation hasn't been added by the dialect. See also https://mlir.llvm.org/getting_started/Faq/#registered-loaded-dependent-whats-up-with-dialects-management
```
Note that even though the sparse runtime support lib always uses SoA
storage for COO storage (and provides correct codegen by means of views
into this storage), in some rare cases we need the true physical SoA
storage as a coordinate buffer. This PR provides that functionality by
means of a (costly) coordinate buffer call.
Since this is currently only used for testing/debugging by means of the
sparse_tensor.print method, this solution is acceptable. If we ever want
a performing version of this, we should truly support AoS storage of COO
in addition to the SoA used right now.
This commit fixes memory leaks in sparse tensor integration tests by
adding `bufferization.dealloc_tensor` ops.
Note: Buffer deallocation will be automated in the future with the
ownership-based buffer deallocation pass, making `dealloc_tensor`
obsolete (only codegen path, not when using the runtime library).
This commit fixes the remaining memory leaks in the MLIR test suite.
`check-mlir` now passes when built with ASAN.
This change lifts the restriction that purely allocated empty sparse
tensors cannot escape the method. Instead it makes a best effort to add
a finalizing operation before the escape.
This assumes that
(1) we never build sparse tensors across method boundaries
(e.g. allocate in one, insert in other method)
(2) if we have other uses of the empty allocation in the
same method, we assume that either that op will fail
or will do the finalization for us.
This is best-effort, but fixes some very obvious missing cases.
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`.
This commit fixes memory leaks in sparse tensor integration tests by
adding `bufferization.dealloc_tensor` ops.
Note: Buffer deallocation will be automated in the future with the
ownership-based buffer deallocation pass, making `dealloc_tensor`
obsolete (only codegen path, not when using the runtime library).
This is first step (of many) cleaning up our tests to use the new and
exciting sparse_tensor.print operation instead of lengthy extraction +
print ops.
This commit fixes memory leaks in sparse tensor integration tests by
adding `bufferization.dealloc_tensor` ops.
Note: Buffer deallocation will be automated in the future with the
ownership-based buffer deallocation pass, making `dealloc_tensor`
obsolete (only codegen path, not when using the runtime library).
This commit fixes memory leaks in sparse tensor integration tests by
adding `bufferization.dealloc_tensor` ops.
Note: Buffer deallocation will be automated in the future with the
ownership-based buffer deallocation pass, making `dealloc_tensor`
obsolete (only codegen path, not when using the runtime library).
…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.
1. Add python test for n out of m
2. Add more methods for python binding
3. Add verification for n:m and invalid encoding tests
4. Add e2e test for n:m
Previous PRs for n:m #80501#79935
