* 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
Tian Jin
GHEORGHE-TEOD BERCEA
Tian Jin