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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
The LLVM Compiler Infrastructure
This directory and its sub-directories contain source code for LLVM, a toolkit for the construction of highly optimized compilers, optimizers, and run-time environments.
The README briefly describes how to get started with building LLVM. For more information on how to contribute to the LLVM project, please take a look at the Contributing to LLVM guide.
Getting Started with the LLVM System
Taken from https://llvm.org/docs/GettingStarted.html.
Overview
Welcome to the LLVM project!
The LLVM project has multiple components. The core of the project is itself called "LLVM". This contains all of the tools, libraries, and header files needed to process intermediate representations and converts it into object files. Tools include an assembler, disassembler, bitcode analyzer, and bitcode optimizer. It also contains basic regression tests.
C-like languages use the Clang front end. This component compiles C, C++, Objective-C, and Objective-C++ code into LLVM bitcode -- and from there into object files, using LLVM.
Other components include: the libc++ C++ standard library, the LLD linker, and more.
Getting the Source Code and Building LLVM
The LLVM Getting Started documentation may be out of date. The Clang Getting Started page might have more accurate information.
This is an example work-flow and configuration to get and build the LLVM source:
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Checkout LLVM (including related sub-projects like Clang):
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git clone https://github.com/llvm/llvm-project.git -
Or, on windows,
git clone --config core.autocrlf=false https://github.com/llvm/llvm-project.git
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Configure and build LLVM and Clang:
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cd llvm-project -
mkdir build -
cd build -
cmake -G <generator> [options] ../llvmSome common build system generators are:
Ninja--- for generating Ninja build files. Most llvm developers use Ninja.Unix Makefiles--- for generating make-compatible parallel makefiles.Visual Studio--- for generating Visual Studio projects and solutions.Xcode--- for generating Xcode projects.
Some Common options:
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-DLLVM_ENABLE_PROJECTS='...'--- semicolon-separated list of the LLVM sub-projects you'd like to additionally build. Can include any of: clang, clang-tools-extra, libcxx, libcxxabi, libunwind, lldb, compiler-rt, lld, polly, or debuginfo-tests.For example, to build LLVM, Clang, libcxx, and libcxxabi, use
-DLLVM_ENABLE_PROJECTS="clang;libcxx;libcxxabi". -
-DCMAKE_INSTALL_PREFIX=directory--- Specify for directory the full path name of where you want the LLVM tools and libraries to be installed (default/usr/local). -
-DCMAKE_BUILD_TYPE=type--- Valid options for type are Debug, Release, RelWithDebInfo, and MinSizeRel. Default is Debug. -
-DLLVM_ENABLE_ASSERTIONS=On--- Compile with assertion checks enabled (default is Yes for Debug builds, No for all other build types).
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cmake --build . [-- [options] <target>]or your build system specified above directly.-
The default target (i.e.
ninjaormake) will build all of LLVM. -
The
check-alltarget (i.e.ninja check-all) will run the regression tests to ensure everything is in working order. -
CMake will generate targets for each tool and library, and most LLVM sub-projects generate their own
check-<project>target. -
Running a serial build will be slow. To improve speed, try running a parallel build. That's done by default in Ninja; for
make, use the option-j NNN, whereNNNis the number of parallel jobs, e.g. the number of CPUs you have.
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For more information see CMake
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Consult the Getting Started with LLVM page for detailed information on configuring and compiling LLVM. You can visit Directory Layout to learn about the layout of the source code tree.