aligned_alloc was added in MacOS 10.15, some users want to support older
versions. The runtime functions makes this easy, so just put in a call
to posix_memalign, which provides the same functionality.
These functions don't depend on the C++ runtime and therefore belong to
CRunnerUtils. Clean up the macros on the way as `_MSC_VER` indicates the
compiler, not the platform, which is indicated by `_WIN32` and will be
present when, e.g., compiling with minGW.
Reviewed By: rdzhabarov
Differential Revision: https://reviews.llvm.org/D130025
When converted to the LLVM dialect, the memref.alloc and memref.free operations were generating calls to hardcoded 'malloc' and 'free' functions. This didn't leave any freedom to users to provide their custom implementation. Those operations now convert into calls to '_mlir_alloc' and '_mlir_free' functions, which have also been implemented into the runtime support library as wrappers to 'malloc' and 'free'. The same has been done for the 'aligned_alloc' function.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D128791
Currently, there've been a lot of warnings while building MLIR.
This change fixes the warnings listed below.
.../SparseTensorUtils.cpp: In instantiation of ‘...::openSparseTensorCOO(...) [with ...]’:
.../SparseTensorUtils.cpp:1672:3: required from here
.../SparseTensorUtils.cpp:87:21: warning: format ‘%d’ expects argument of type ‘int’, but argument 3 has type ‘PrimaryType’ [-Wformat=]
.../OptUtils.cpp:36:5: warning: this statement may fall through [-Wimplicit-fallthrough=]
.../AffineOps.cpp:1741:32: warning: suggest parentheses around ‘&&’ within ‘||’ [-Wparentheses]
Reviewed By: aartbik, wrengr, aeubanks
Differential Revision: https://reviews.llvm.org/D128993
This adds weak versions of the truncation libcalls in case the runtime
environment doesn't have them.
Differential Revision: https://reviews.llvm.org/D128091
This fixes all sorts of ABI issues due to passing by-value
(using by-reference with memref's exclusively).
Reviewed By: bkramer
Differential Revision: https://reviews.llvm.org/D128018
Support complex numbers for Matrix Market Exchange Formats. Add a test case.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D127138
This is the first PR to add `F16` and `BF16` support to the sparse codegen. There are still problems in supporting these two data types, such as `BF16` is not quite working yet.
Add tests cases.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D127010
The previous macro definition using `{...}` would fail to compile when the callsite uses a semicolon followed by an else-statement (i.e., `if (...) FATAL(...); else ...;`). Replacing the simple braces with `do{...}while(0)` (n.b., semicolon not included in the macro definition) enables callsites to use the semicolon plus else-statement syntax without problems. The new definition now requires the semicolon at all callsites, but since it was already being called that way nothing changes.
For more explanation, see <https://gcc.gnu.org/onlinedocs/cpp/Swallowing-the-Semicolon.html>
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D126514
The primary goal of this change is to define readSparseTensorShape. Whereas the SparseTensorFile class is merely introduced as a way to reduce code duplication along the way.
Depends On D126106
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D126233
The semicolons were introduced in D126105 in order to correct clang-format, but I forgot this file must be compiled as C++98 rather than C++11.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D126561
This is a followup to D126105 to move functions in SparseTensorUtils.cpp to match their locations in SparseTensorUtils.h
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D126106
This change makes the public API of SparseTensorUtils.cpp explicit, whereas before the publicity of these functions was only implicit. Implicit publicity is sufficient for mlir-opt to generate calls to these functions, but it's not enough to enable C/C++ code to call them directly in the usual way (i.e., without going through codegen). Thus, leaving the publicity implicit prevents development of other tools (e.g., microbenchmarks).
In addition this change also marks the functions MLIR_CRUNNERUTILS_EXPORT, which is required by the JIT under certain configurations (albeit not for anything in our test suite).
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D126105
By closing over the `rank` itself rather than `this`, we save a method call on each iteration. A minor optimization, but one that adds up.
Depends On D126016
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D126019
This is a followup to D125431, to keep from confusing the machinery that generates diffs (since combining these two changes into one would obfuscate the changes actually made in the previous differential).
Depends On D125431
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D125432
In addition to reducing code repetition, this also helps ensure that the various API functions follow the naming convention of mlir::sparse_tensor::primaryTypeFunctionSuffix (e.g., due to typos in the repetitious code).
Depends On D125428
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D125431
This enables the compiler to perform devirtualization. And benchmarks
indicate devirtualization can sometimes give considerable speedup.
Depends On D122061
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D125428
This is the first implementation of complex (f64 and f32) support
in the sparse compiler, with complex add/mul as first operations.
Note that various features are still TBD, such as other ops, and
reading in complex values from file. Also, note that the
std::complex<float> had a bit of an ABI issue when passed as
single argument. It is still TBD if better solutions are possible.
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D125596
D125214 split off a MLIRExecutionEngineUtils library that is used
by MLIRGPUTransforms. However, currently the entire ExecutionEngine
directory is skipped if the LLVM_NATIVE_ARCH target is not available.
Move the check for LLVM_NATIVE_ARCH, such that MLIRExecutionEngineUtils
always gets built, and only the JIT-related libraries are omitted
without native arch.
Differential Revision: https://reviews.llvm.org/D125357
Building libMLIR.so currently fails with:
> /usr/bin/ld: /tmp/ccNzulEA.ltrans39.ltrans.o: in function `(anonymous namespace)::SerializeToHsacoPass::optimizeLlvm(llvm::Module&, llvm::TargetMachine&)':
> /builddir/build/BUILD/llvm-project-15.0.0.src/mlir/lib/Dialect/GPU/Transforms/SerializeToHsaco.cpp:328: undefined reference to `mlir::makeOptimizingTransformer(unsigned int, unsigned int, llvm::TargetMachine*)'
This is because MLIRGPUTransforms depends on MLIRExecutionEngine in
61bb2e4ea8/mlir/lib/Dialect/GPU/Transforms/SerializeToHsaco.cpp (L328),
but MLIRExecutionEngine is marked as excluded from libMLIR.so.
However, this code doesn't require the full execution engine: It
only performs middle-end optimization, and does not need any of
the JIT/codegen infrastructure. As such, split off a separate
library MLIRExecutionEngineUtils, which only contains that part
and is not excluded from libMLIR.so.
Fixes https://github.com/llvm/llvm-project/issues/54242.
Differential Revision: https://reviews.llvm.org/D125214
The names of the functions that are supposed to be exported do not match the implementations. This is due in part to cac7aabbd8.
This change makes the implementations and declarations match and adds a couple missing declarations.
The new names follow the pattern of the existing `verify` functions where the prefix is maintained as `_mlir_ciface_` but the suffix follows the new naming convention.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D124891
This will enable our usual set of element types in external
environments, such as PyTACO support.
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D124875
By using a shared index pool, we reduce the footprint of each "Element"
in the COO scheme and, in addition, reduce the overhead of allocating
indices (trading many allocations of vectors for allocations in a single
vector only). When the capacity is known, this means *all* allocation
can be done in advance.
This is a big win. For example, reading matrix SK-2005, with dimensions
50,636,154 x 50,636,154 and 1,949,412,601 nonzero elements improves
as follows (time in ms), or about 3.5x faster overall
```
SK-2005 before after speedup
---------------------------------------------
read 305,086.65 180,318.12 1.69
sort 2,836,096.23 510,492.87 5.56
pack 364,485.67 312,009.96 1.17
---------------------------------------------
TOTAL 3,505,668.56 1,002,820.95 3.50
```
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D124502
Kazu Hirata