This does not change the behavior directly: the tests only run when
`-DMLIR_INCLUDE_INTEGRATION_TESTS=ON` is configured. However running
`ninja check-mlir` will not run all the tests within a single
lit invocation. The previous behavior would wait for all the integration
tests to complete before starting to run the first regular test. The
test results were also reported separately. This change is unifying all
of this and allow concurrent execution of the integration tests with
regular non-regression and unit-tests.
Differential Revision: https://reviews.llvm.org/D97241
Multi-configuration generators (such as Visual Studio and Xcode) allow the specification of a build flavor at build time instead of config time, so the lit configuration files need to support that - and they do for the most part. There are several places that had one of two issues (or both!):
1) Paths had %(build_mode)s set up, but then not configured, resulting in values that would not work correctly e.g. D:/llvm-build/%(build_mode)s/bin/dsymutil.exe
2) Paths did not have %(build_mode)s set up, but instead contained $(Configuration) (which is the value for Visual Studio at configuration time, for Xcode they would have had the equivalent) e.g. "D:/llvm-build/$(Configuration)/lib".
This seems to indicate that we still have a lot of fragility in the configurations, but also that a number of these paths are never used (at least on Windows) since the errors appear to have been there a while.
This patch fixes the configurations and it has been tested with Ninja and Visual Studio to generate the correct paths. We should consider removing some of these settings altogether.
Reviewed By: JDevlieghere, mehdi_amini
Differential Revision: https://reviews.llvm.org/D96427
07f1047f41 changed the CMake detection to use find_package(Python3 ...
but didn't update the lit configuration to use the expected Python3_EXECUTABLE
cmake variable to point to the interpreter path.
This resulted in an empty path on MacOS.
This patch introduces a SPIR-V runner. The aim is to run a gpu
kernel on a CPU via GPU -> SPIRV -> LLVM conversions. This is a first
prototype, so more features will be added in due time.
- Overview
The runner follows similar flow as the other runners in-tree. However,
having converted the kernel to SPIR-V, we encode the bind attributes of
global variables that represent kernel arguments. Then SPIR-V module is
converted to LLVM. On the host side, we emulate passing the data to device
by creating in main module globals with the same symbolic name as in kernel
module. These global variables are later linked with ones from the nested
module. We copy data from kernel arguments to globals, call the kernel
function from nested module and then copy the data back.
- Current state
At the moment, the runner is capable of running 2 modules, nested one in
another. The kernel module must contain exactly one kernel function. Also,
the runner supports rank 1 integer memref types as arguments (to be scaled).
- Enhancement of JitRunner and ExecutionEngine
To translate nested modules to LLVM IR, JitRunner and ExecutionEngine were
altered to take an optional (default to `nullptr`) function reference that
is a custom LLVM IR module builder. This allows to customize LLVM IR module
creation from MLIR modules.
Reviewed By: ftynse, mravishankar
Differential Revision: https://reviews.llvm.org/D86108
Summary:
* Native '_mlir' extension module.
* Python mlir/__init__.py trampoline module.
* Lit test that checks a message.
* Uses some cmake configurations that have worked for me in the past but likely needs further elaboration.
Subscribers: mgorny, mehdi_amini, rriddle, jpienaar, shauheen, antiagainst, nicolasvasilache, arpith-jacob, mgester, lucyrfox, aartbik, liufengdb, stephenneuendorffer, Joonsoo, grosul1, Kayjukh, jurahul, msifontes
Tags: #mlir
Differential Revision: https://reviews.llvm.org/D83279
Summary:
`mlir-rocm-runner` is introduced in this commit to execute GPU modules on ROCm
platform. A small wrapper to encapsulate ROCm's HIP runtime API is also inside
the commit.
Due to behavior of ROCm, raw pointers inside memrefs passed to `gpu.launch`
must be modified on the host side to properly capture the pointer values
addressable on the GPU.
LLVM MC is used to assemble AMD GCN ISA coming out from
`ConvertGPUKernelToBlobPass` to binary form, and LLD is used to produce a shared
ELF object which could be loaded by ROCm HIP runtime.
gfx900 is the default target be used right now, although it could be altered via
an option in `mlir-rocm-runner`. Future revisions may consider using ROCm Agent
Enumerator to detect the right target on the system.
Notice AMDGPU Code Object V2 is used in this revision. Future enhancements may
upgrade to AMDGPU Code Object V3.
Bitcode libraries in ROCm-Device-Libs, which implements math routines exposed in
`rocdl` dialect are not yet linked, and is left as a TODO in the logic.
Reviewers: herhut
Subscribers: mgorny, tpr, dexonsmith, mehdi_amini, rriddle, jpienaar, shauheen, antiagainst, nicolasvasilache, csigg, arpith-jacob, mgester, lucyrfox, aartbik, liufengdb, stephenneuendorffer, Joonsoo, grosul1, frgossen, Kayjukh, jurahul, llvm-commits
Tags: #mlir, #llvm
Differential Revision: https://reviews.llvm.org/D80676
Make ConvertKernelFuncToCubin pass to be generic:
- Rename to ConvertKernelFuncToBlob.
- Allow specifying triple, target chip, target features.
- Initializing LLVM backend is supplied by a callback function.
- Lowering process from MLIR module to LLVM module is via another callback.
- Change mlir-cuda-runner to adopt the revised pass.
- Add new tests for lowering to ROCm HSA code object (HSACO).
- Tests for CUDA and ROCm are kept in separate directories.
Differential Revision: https://reviews.llvm.org/D80142
Add an initial version of mlir-vulkan-runner execution driver.
A command line utility that executes a MLIR file on the Vulkan by
translating MLIR GPU module to SPIR-V and host part to LLVM IR before
JIT-compiling and executing the latter.
Differential Revision: https://reviews.llvm.org/D72696
Moving cuda-runtime-wrappers.so into subdirectory to match libmlir_runner_utils.so.
Provide parent directory when running test and load .so from subdirectory.
PiperOrigin-RevId: 282410749
This tool allows to execute MLIR IR snippets written in the GPU dialect
on a CUDA capable GPU. For this to work, a working CUDA install is required
and the build has to be configured with MLIR_CUDA_RUNNER_ENABLED set to 1.
PiperOrigin-RevId: 256551415
The actual transformation from PTX source to a CUDA binary is now factored out,
enabling compiling and testing the transformations independently of a CUDA
runtime.
MLIR has still to be built with NVPTX target support for the conversions to be
built and tested.
PiperOrigin-RevId: 255167139
This CL performs post-commit cleanups.
It adds the ability to specify which shared libraries to load dynamically in ExecutionEngine. The linalg integration test is updated to use a shared library.
Additional minor cleanups related to LLVM lowering of Linalg are also included.
--
PiperOrigin-RevId: 248346589
Christian Sigg