* Detect llvm-project commit change in utils/clone-mlir.sh and rebuild llvm-project
for zLinux Jenkins build bot
* Cleanup RtMemRef API
- use forward declaration to hide private data fields
- RtMemRef.h: external user header, C/C++
- _RtMemRef.h: internal user header, C++ only
- RtMemRef.hpp and RtMemRef.cpp: implementation header and file
- add external APIs
OrderedRtMemRefDict *ormrd_create(RtMemRef **rmrs, int n)
RtMemRef **ormrd_getRmrs(OrderedRtMemRefDict *ormrd)
int ormrd_getNumOfRmrs(OrderedRtMemRefDict *ormrd)
for creating and querying OrderedRtMemRefDict with RtMemRef arrays
- data buffer installed by rmr_setData() will be managed by user
- unique_ptr<RtMemRef> must use custom deleter <RtMemRef,decltype(&rmr_destroy)>
* See if I have write access.
* Remove test CMake code.
* Use new API.
* Format code.
* Format code & rename variables for readability.
* Remove used API spec.
* Rename OrderedRtMemRefDict -> RtMemRefList, _dataMalloc -> _owningData.
* OrderedRtMemRefDict -> RtMemRefList
* Update KrnlToLLVM.cpp
* Trigger Jenkins
* Restart Jenkins
* OrderedRtMemRefDict -> RtRmrRefList
* More OrderedRtMemRefDict -> RtMemRefList.
* Format jni wrapper.
* Rename API functions to maintain stylistic consistency.
* Bug fix.
* Bug fix.
* Format code.
* Fix RtMemRefUtils.
* Format code.
* Using llvm function naming scheme.
* Rename runtime api file name to project name (onnx-mlir) as per convention.
* Include the new runtime header file.
* Reflect api header file name change in build script.
* Bug fix.
* Remove C++ code.
* Revert "Remove C++ code."
This reverts commit b217dfabae99e42db30721600cb5507866d4dc98.
* Clarify memory management responsibility.
* Add constructor to specify name & data ownership.
* Include stdbool.
* Remove dictionary semantics from RtMemRefList
* Bug fix.
* Format code.
* Format code.
* Use macro to define database of metadata.
* Prevent formatter from acting on metadata decl.
* Nit.
* Restore backend unit tests.
* Use spaces instead of tabs for better formatting.
* Use explicit template instantiation.
* Update RtMemRef struct doc.
* Make runtime compilable both in c and c++ mode.
Build two versions of the runtime library, one c version as the user-facing c runtime, and one c++ version as the one used inside this project.
* Bug fix, avoid stack allocation for output rmr list.
* Change _dyn_entry_point_main_graph -> run_main_graph for better memorability.
* Write a complete introductory tutorial on c99 Runtime and a test for it.
* Add onnx installation as dependency.
* Use target_include_directory to avoid installation.
* Format code.
* Fix cmake target_include_directories.
* Address compiler warning.
* First pass of RtMemRef->OMTensor.
* Second pass of RtMemRef -> OMTensor.
* nit, omtList -> omTensorList.
* omt -> omTensor for clarity.
* Rename OnnxMlirInternal.h -> OnnxMlirRuntime.hpp because there's no internal/external API, only C/C++ API.
* Format code.
* Restructure Runtime source code and move header -> /include and test -> /test/unit.
* Bugfix.
* Format code.
* Add unit test for OMTensor ctor.
* Update JNI CMake include directory.
* Bugfix.
* No need to re-declare ONNX types.
* Disable runtime doc test on non-x86 platforms.
* Rename OMTensor fields to be more sensible.
* Fix data type mismatch.
* size_t -> int64_t, prefer fixed width integers.
* Use consistent header guard style.
* Further tweak OMTensor API.
* Bugfix.
* Bugfix.
* Format code.
* Add doxygen config file.
* Tweak OMTensor API.
* Tweak API doc, hide OMTensorList implementation.
* Format code.
* Add new documentation item for Runtime API.
* Hide internal use only API declarations, move their comments to their implementations.
* Clarify ownership semantics in relevant API documentations.
* Fix PyRuntime.
* Remove alignment concerns from public API and include explaination of alignment issue in struct OMTensor definition.
* Print out unsupported numpy dtype.
* Use preferred way of type comparison in pybind11.
* Debug s390x issue.
* Remove debug code.
* Clarify semantics of strides/shape setter/getter, use \brief to include short description of API function.
* Improve documentation.
* Single out unpolished C++ API declarations.
* Clarify OMTensorList API.
* Bugfix.
* Bugfix.
* Assert after malloc.
* Handle malloc failures.
* Nit.
* Tweak legal notices.
* Format code.
* Remove doxygen generated files.
* Tweak legal notice format.
* Upgrade Cython
Numpy installation depends on Cython.
Co-authored-by: Tian Jin <tjingrant@gmail.com>
* Call llc, ld from within onnx-mlir.
* Rename EmitLLVMBC -> EmitLib., reorder header files
* Edit comment.
* Checkpoint, debug.py works.
* Automatically generate inputs in debug.py.
* Use float.
* initial support for rapidcheck tests.
* Convolution test case works.
* Format code.
* Link library with MainUtils.
* Fix CMake script error.
* Fast implementation of array assertion, more detailed error analysis.
* More utility for DynMemRef.
* Fix linking issue.
* Uncomment unit test.
* Refactor to separate C++/Python ExecutionSession, enable unit test.
* format code.
* Verbose build.
* Enable PIC option for ExecusionSession.
* Fix cmake error.
* Build all targets.
* Fix doc to build all targets.
* Clean up.
* Clean up, debug.
* Use type alias consistently.
* Move definitions to DynMemRef.cpp.
* include algorithm.
* pyruntime -> PyRuntime
* Format code.
* Free memory.
* Add comments.
* Copyright notice.
* Improve stylistic consistency.
* Add comment.
* Revert irrelevant changes.
* Disambiguate.
* Refator test case generator out from test case implementation, implement example exhaustive test driver.
* Add documentation for testing.
* Sync with latest MLIR.
* Enable ONNX backend tests as a means to test ONNF lowering end-to-end.
* Install ONNX using quiet mode.
* Remove debug comments.
* Install ONNX from third_party/onnx.
* Check python version and fix pip command for installing ONNX.
* Using --user install option to prevent permission denied.
* Remove unused imports.
* Try using stock ONNX pip package as there are more tests in them.
* Pip got stuck building wheels, try sudo.
* Use verbose install to debug.
* Invalidate cache to build LLVM tools.
* Fix mlir installation script location.
* Debug to locate ONNF.
* Sanity check.
* Check out ONNF code first.
* Use verbose LIT output.
* 1. Update documentation to always use verbose LIT.
2. Update krnl ops to reflect new affine map attribute syntax.
* See if conda exists
* Install ONNX by manually cloning the repo.
* Install cmake first.
* Using sudo priviledge when installing.
* Limit build parallelism.
* Limit parallelism.
* Larger memory.
* Install onnx package with pip.
* Build MLIR tools.
* Invalidate cache.
* Compile model.so with -fPIC.
* Remove module dump to get concise debug output.
* Print command before executing.
* Use quiet install mode to reduce logging.
* Use -relocation-model=pic to generate position independent code.
* 1. Remove MAKEFLAGS because now buildbot has enough memory.
2. Run DocCheck as a last step.
* 1. Add verbose mode for backtend test.
* When dumping to LLVM bitcode, do not dump module IR, but print a message indicating that bitcode has been written to disk.
* Do not pass MakeFlags to CMake.
* Add more explaination for posible reasons of failing to identify tests.
* compartmentalize build script, temporarily remove dependency of onnf_opt on helper.cpp
* fix test includes
* fix op directory include
* compiler -> op
* compiler test depends on boost system
* fix function name
* specify libcompiler dependencies
* let cmake take care of transitive dependencies
* remove unnecessary includes
* use ONNF_SRC_ROOT and ONNF_BIN_ROOT
* allow whole-archive linked libraries to be appended
* [MLIR] Support filecheck (#371)
* support lit+FileCheck
* add lit into build script
* format MLIR.cmake
* format cmake
* [MLIR] Remove input/output ops (#372)
* remove input/output ops
* get output tensor type from symbol table
gongsu832
Tian Jin
GHEORGHE-TEOD BERCEA
Tian Jin