This option tells CMake to add current source and binary
directories to the include path for each directory[1].
Required include directories from build tree (for generated
files) were previously added in `mlir_tablegen` but this was
changed in 03078ec20b .
These are still needed, however, for out-of-tree builds
that don't build as part of LLVM (via LLVM_ENABLE_PROJECTS).
Building as part of LLVM works regardless, AFAICT,
because LLVM sets this option and so the MLIR build inherits it.
FWIW, various other (in-tree) LLVM projects set this as well.
And of course this fixes the out-of-tree
mlir-by-itself build scenario I'm using.
[1] https://cmake.org/cmake/help/latest/variable/CMAKE_INCLUDE_CURRENT_DIR.html
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D122088
This adds an option to configure the CMake python search priming
behaviour that was introduced in D118148. In some environments the
priming would cause the "real" search to fail. The default behaviour is
unchanged, i.e. the search will be primed.
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D120765
This is is the start of the MLIR benchmarks. It sets up a command
line tool along with conventions to define and run benchmarks
using mlir's python bindings.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D115174
Its defaulting logic must go after `project(..)` to work correctly, but `project(..)` is often in a standalone condition making this
awkward, since the rest of the condition code may also need GNUInstallDirs.
The good thing is there are the various standalone booleans, which I had missed before. This makes splitting the conditional blocks less awkward.
Reviewed By: arichardson, phosek, beanz, ldionne, #libunwind, #libc, #libc_abi
Differential Revision: https://reviews.llvm.org/D117639
This is the original patch in my GNUInstallDirs series, now last to merge as the final piece!
It arose as a new draft of D28234. I initially did the unorthodox thing of pushing to that when I wasn't the original author, but since I ended up
- Using `GNUInstallDirs`, rather than mimicking it, as the original author was hesitant to do but others requested.
- Converting all the packages, not just LLVM, effecting many more projects than LLVM itself.
I figured it was time to make a new revision.
I have used this patch series (and many back-ports) as the basis of https://github.com/NixOS/nixpkgs/pull/111487 for my distro (NixOS), which was merged last spring (2021). It looked like people were generally on board in D28234, but I make note of this here in case extra motivation is useful.
---
As pointed out in the original issue, a central tension is that LLVM already has some partial support for these sorts of things. Variables like `COMPILER_RT_INSTALL_PATH` have already been dealt with. Variables like `LLVM_LIBDIR_SUFFIX` however, will require further work, so that we may use `CMAKE_INSTALL_LIBDIR`.
These remaining items will be addressed in further patches. What is here is now rote and so we should get it out of the way before dealing more intricately with the remainder.
Reviewed By: #libunwind, #libc, #libc_abi, compnerd
Differential Revision: https://reviews.llvm.org/D99484
This is the original patch in my GNUInstallDirs series, now last to merge as the final piece!
It arose as a new draft of D28234. I initially did the unorthodox thing of pushing to that when I wasn't the original author, but since I ended up
- Using `GNUInstallDirs`, rather than mimicking it, as the original author was hesitant to do but others requested.
- Converting all the packages, not just LLVM, effecting many more projects than LLVM itself.
I figured it was time to make a new revision.
I have used this patch series (and many back-ports) as the basis of https://github.com/NixOS/nixpkgs/pull/111487 for my distro (NixOS), which was merged last spring (2021). It looked like people were generally on board in D28234, but I make note of this here in case extra motivation is useful.
---
As pointed out in the original issue, a central tension is that LLVM already has some partial support for these sorts of things. Variables like `COMPILER_RT_INSTALL_PATH` have already been dealt with. Variables like `LLVM_LIBDIR_SUFFIX` however, will require further work, so that we may use `CMAKE_INSTALL_LIBDIR`.
These remaining items will be addressed in further patches. What is here is now rote and so we should get it out of the way before dealing more intricately with the remainder.
Reviewed By: #libunwind, #libc, #libc_abi, compnerd
Differential Revision: https://reviews.llvm.org/D99484
See the docs in the new function for details.
I think I found every instance of this copy pasted code. Polly could
also use it, but currently does something different, so I will save the
behavior change for a future revision.
We get the shared, non-installed CMake modules following the pattern
established in D116472.
It might be good to have LLD and Flang also use this, but that would be
a functional change and so I leave it as future work.
Reviewed By: beanz, lebedev.ri
Differential Revision: https://reviews.llvm.org/D116521
This is a defensive action to catch at build time on Linux failures that
may happen only on Windows otherwise.
Differential Revision: https://reviews.llvm.org/D115316
Per discussion on discord and various feature requests across bindings (Haskell and Rust bindings authors have asked me directly), we should be building a link-ready MLIR-C dylib which exports the C API and can be used without linking to anything else.
This patch:
* Adds a new MLIR-C aggregate shared library (libMLIR-C.so), which is similar in name and function to libLLVM-C.so.
* It is guarded by the new CMake option MLIR_BUILD_MLIR_C_DYLIB, which has a similar purpose/name to the LLVM_BUILD_LLVM_C_DYLIB option.
* On all platforms, this will work with both static, BUILD_SHARED_LIBS, and libMLIR builds, if supported:
* In static builds: libMLIR-C.so will export the CAPI symbols and statically link all dependencies into itself.
* In BUILD_SHARED_LIBS: libMLIR-C.so will export the CAPI symbols and have dynamic dependencies on implementation shared libraries.
* In libMLIR.so mode: same as static. libMLIR.so was not finished for actual linking use within the project. An eventual relayering so that libMLIR-C.so depends on libMLIR.so is possible but requires first re-engineering the latter to use the aggregate facility.
* On Linux, exported symbols are filtered to only the CAPI. On others (MacOS, Windows), all symbols are exported. A CMake status is printed unless if global visibility is hidden indicating that this has not yet been implemented. The library should still work, but it will be larger and more likely to conflict until fixed. Someone should look at lifting the corresponding support from libLLVM-C.so and adapting. Or, for special uses, just build with `-DCMAKE_CXX_VISIBILITY_PRESET=hidden -DCMAKE_C_VISIBILITY_PRESET=hidden`.
* Includes fixes to execution engine symbol export macros to enable default visibility. Without this, the advice to use hidden visibility would have resulted in test failures and unusable execution engine support libraries.
Differential Revision: https://reviews.llvm.org/D113731
Introduce the initial support for operation interfaces in C API and Python
bindings. Interfaces are a key component of MLIR's extensibility and should be
available in bindings to make use of full potential of MLIR.
This initial implementation exposes InferTypeOpInterface all the way to the
Python bindings since it can be later used to simplify the operation
construction methods by inferring their return types instead of requiring the
user to do so. The general infrastructure for binding interfaces is defined and
InferTypeOpInterface can be used as an example for binding other interfaces.
Reviewed By: gysit
Differential Revision: https://reviews.llvm.org/D111656
As discussed on discord, we have never actually been able to build with the project-wide published min version of 3.14.3. The buildbot that tests the Python configuration is currently pinned to 3.19.1, and there are a number of non-version/policy controlled features that Python building relies on that makes it unreliable with older versions. Some of the issues are pretty fundamental and I don't know how to do them on the older version. I think that, as an optional feature, at least advertising the PSA as in this patch is a good middle ground until the next project-wide CMake version bump.
Also moves setup logic to a macro so that everyone can use it.
This reverts commit 7aebdfc4fc.
The build is broken with errors like:
GPUPasses.cpp:(.text.pybind11_object_init[pybind11_object_init]+0x118): undefined reference to `PyExc_TypeError'
After CMake 3.18, we are able to limit the scope of the
find_package(Python3 ...) search to just Development.Module. Searching
for Development will fail in manylinux builds, and isn't necessary
since we are not embedding the Python interpreter. For more information, see:
https://pybind11.readthedocs.io/en/stable/compiling.html#findpython-mode
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D111383
Drop old cmake variable names that were kept around so that zorg
buildbot could be migrated, which has now happened (D102977). D102976
had fixed the inconsistent names.
Differential Revision: https://reviews.llvm.org/D102997
Fix inconsistent MLIR CMake variable names. Consistently name them as
MLIR_ENABLE_<feature>.
Eg: MLIR_CUDA_RUNNER_ENABLED -> MLIR_ENABLE_CUDA_RUNNER
MLIR follows (or has mostly followed) the convention of naming
cmake enabling variables in the from MLIR_ENABLE_... etc. Using a
convention here is easy and also important for convenience. A counter
pattern was started with variables named MLIR_..._ENABLED. This led to a
sequence of related counter patterns: MLIR_CUDA_RUNNER_ENABLED,
MLIR_ROCM_RUNNER_ENABLED, etc.. From a naming standpoint, the imperative
form is more meaningful. Additional discussion at:
https://llvm.discourse.group/t/mlir-cmake-enable-variable-naming-convention/3520
Switch all inconsistent ones to the ENABLE form. Keep the couple of old
mappings needed until buildbot config is migrated.
Differential Revision: https://reviews.llvm.org/D102976
* NFC but has some fixes for CMake glitches discovered along the way (things not cleaning properly, co-mingled depends).
* Includes previously unsubmitted fix in D98681 and a TODO to fix it more appropriately in a smaller followup.
Differential Revision: https://reviews.llvm.org/D101493
The lit test suite uses python 3.6 features. Rather than a strange
python syntax error upon running the lit tests, we will require the
correct version in CMake.
Reviewed By: serge-sans-paille, yln
Differential Revision: https://reviews.llvm.org/D95635
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
The CMake changes in 2aa1af9b1d to make it possible to build MLIR as a
standalone project unfortunately disabled all unit-tests from the
regular in-tree build.
Add the necessary bits to CMakeLists to make it possible to configure
MLIR against installed LLVM, and build it with minimal need for LLVM
source tree. The latter is only necessary to run unittests, and if it
is missing then unittests are skipped with a warning.
This change includes the necessary changes to tests, in particular
adding some missing substitutions and defining missing variables
for lit.site.cfg.py substitution.
Reviewed By: stephenneuendorffer
Differential Revision: https://reviews.llvm.org/D85464
Co-authored-by: Isuru Fernando <isuruf@gmail.com>
Previously, CMake would find any version of Python3. However, the project
claims to require 3.6 or greater, and 3.6 features are being used.
Reviewed By: yln
Differential Revision: https://reviews.llvm.org/D95635
Use cross-compilation approach for `mlir-linalg-ods-gen` application
similar to TblGen tools.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D94598
The test process of the ir_array_attributes.py depends on numpy. This patch checks numpy in Python bindings configuration.
- Add NumPy in find_package as a required component to check numpy.
- If numpy is found, print the version and include directory.
Differential Revision: https://reviews.llvm.org/D92276
* Makes `pip install pybind11` do the right thing with no further config.
* Since we now require a version of pybind11 greater than many LTS OS installs (>=2.6), a more convenient way to get a recent version is preferable.
* Also adds the version spec to find_package so it will skip older versions that may be lying around.
* Tested the full matrix of old system install, no system install, pip install and no pip install.
Differential Revision: https://reviews.llvm.org/D91903
In ODS, attributes of an operation can be provided as a part of the "arguments"
field, together with operands. Such attributes are accepted by the op builder
and have accessors generated.
Implement similar functionality for ODS-generated op-specific Python bindings:
the `__init__` method now accepts arguments together with operands, in the same
order as in the ODS `arguments` field; the instance properties are introduced
to OpView classes to access the attributes.
This initial implementation accepts and returns instances of the corresponding
attribute class, and not the underlying values since the mapping scheme of the
value types between C++, C and Python is not yet clear. Default-valued
attributes are not supported as that would require Python to be able to parse
C++ literals.
Since attributes in ODS are tightely related to the actual C++ type system,
provide a separate Tablegen file with the mapping between ODS storage type for
attributes (typically, the underlying C++ attribute class), and the
corresponding class name. So far, this might look unnecessary since all names
match exactly, but this is not necessarily the cases for non-standard,
out-of-tree attributes, which may also be placed in non-default namespaces or
Python modules. This also allows out-of-tree users to generate Python bindings
without having to modify the bindings generator itself. Storage type was
preferred over the Tablegen "def" of the attribute class because ODS
essentially encodes attribute _constraints_ rather than classes, e.g. there may
be many Tablegen "def"s in the ODS that correspond to the same attribute type
with additional constraints
The presence of the explicit mapping requires the change in the .td file
structure: instead of just calling the bindings generator directly on the main
ODS file of the dialect, it becomes necessary to create a new file that
includes the main ODS file of the dialect and provides the mapping for
attribute types. Arguably, this approach offers better separability of the
Python bindings in the build system as the main dialect no longer needs to know
that it is being processed by the bindings generator.
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D91542
Introduce an ODS/Tablegen backend producing Op wrappers for Python bindings
based on the ODS operation definition. Usage:
mlir-tblgen -gen-python-op-bindings -Iinclude <path/to/Ops.td> \
-bind-dialect=<dialect-name>
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D90960
This is useful in C source files where it is easy for a typo to be
silently assumed by the compiler to be an implicit declaration.
Differential Revision: https://reviews.llvm.org/D90727
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
This reverts commit e9b87f43bd.
There are issues with macros generating macros without an obvious simple fix
so I'm going to revert this and try something different.
New projects (particularly out of tree) have a tendency to hijack the existing
llvm configuration options and build targets (add_llvm_library,
add_llvm_tool). This can lead to some confusion.
1) When querying a configuration variable, do we care about how LLVM was
configured, or how these options were configured for the out of tree project?
2) LLVM has lots of defaults, which are easy to miss
(e.g. LLVM_BUILD_TOOLS=ON). These options all need to be duplicated in the
CMakeLists.txt for the project.
In addition, with LLVM Incubators coming online, we need better ways for these
incubators to do things the "LLVM way" without alot of futzing. Ideally, this
would happen in a way that eases importing into the LLVM monorepo when
projects mature.
This patch creates some generic infrastructure in llvm/cmake/modules and
refactors MLIR to use this infrastructure. This should expand to include
add_xxx_library, which is by far the most complicated bit of building a
project correctly, since it has to deal with lots of shared library
configuration bits. (MLIR currently hijacks the LLVM infrastructure for
building libMLIR.so, so this needs to get refactored anyway.)
Differential Revision: https://reviews.llvm.org/D85140
Introduce an initial version of C API for MLIR core IR components: Value, Type,
Attribute, Operation, Region, Block, Location. These APIs allow for both
inspection and creation of the IR in the generic form and intended for wrapping
in high-level library- and language-specific constructs. At this point, there
is no stability guarantee provided for the API.
Reviewed By: stellaraccident, lattner
Differential Revision: https://reviews.llvm.org/D83310
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:
Previous submit of new tests accidentally made this ON.
The tests should be opt-in.
To build with MLIR integration tests enabled, pass the following
cmake .... \
-DMLIR_INCLUDE_INTEGRATION_TESTS=ON \
....
Reviewers: mehdi_amini
Subscribers: mgorny, mehdi_amini, rriddle, jpienaar, shauheen, antiagainst, nicolasvasilache, arpith-jacob, mgester, lucyrfox, liufengdb, stephenneuendorffer, Joonsoo, grosul1, frgossen, Kayjukh, jurahul, msifontes
Tags: #mlir
Differential Revision: https://reviews.llvm.org/D81878
Summary:
This CL introduces an integration test directory for MLIR in general, with
vector dialect integration tests in particular as a first working suite. To
run all the integration tests (and currently just the vector suite):
$ cmake --build . --target check-mlir-integration
[0/1] Running the MLIR integration tests
Testing Time: 0.24s
Passed: 22
The general call is to contribute to this integration test directory with more
tests and other suites, running end-to-end examples that may be too heavy for
the regular test directory, but should be tested occasionally to verify the
health of MLIR.
Background discussion at:
https://llvm.discourse.group/t/vectorops-rfc-add-suite-of-integration-tests-for-vector-dialect-operations/1213/
Reviewers: nicolasvasilache, reidtatge, andydavis1, rriddle, ftynse, mehdi_amini, jpienaar, stephenneuendorffer
Reviewed By: nicolasvasilache, stephenneuendorffer
Subscribers: mgorny, mehdi_amini, rriddle, jpienaar, shauheen, antiagainst, nicolasvasilache, arpith-jacob, mgester, lucyrfox, liufengdb, stephenneuendorffer, Joonsoo, grosul1, frgossen, Kayjukh, jurahul, msifontes
Tags: #mlir
Differential Revision: https://reviews.llvm.org/D81626
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
Will Dietz