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This is part of the RFC for a better fold API: https://discourse.llvm.org/t/rfc-a-better-fold-api-using-more-generic-adaptors/67374 This patch implements the generation of generic adaptors through TableGen. These are essentially a generalization of Adaptors, as implemented previously, but instead of indexing into a `mlir::ValueRange`, they may index into any container, regardless of the element type. This allows the use of the convenient getter methods of Adaptors to be reused on ranges that are the result of some kind of mapping functions of an ops operands. In the case of the fold API in the RFC, this would be `ArrayRef<Attribute>`, which is a mapping of the operands to their possibly-constant values. Implementation wise, some special care was taken to not cause a compile time regression, nor to break any kind of source compatibility. For that purpose, the current adaptor class was split into three: * A generic adaptor base class, within the detail namespace as it is an implementation detail, which implements all APIs independent of the range type used for the operands. This is all the attribute and region related code. Since it is not templated, its implementation does not have to be inline and can be put into the cpp source file * The actual generic adaptor, which has a template parameter for the range that should be indexed into for retrieving operands. It implements all the getters for operands, as they are dependent on the range type. It publicly inherits from the generic adaptor base class * A class named as adaptors have been named so far, inheriting from the generic adaptor class with `mlir::ValueRange` as range to index into. It implements the rest of the API, specific to `mlir::ValueRange` adaptors, which have previously been part of the adaptor. This boils down to a constructor from the Op type as well as the verify function. The last class having the exact same API surface and name as Adaptors did previously leads to full source compatibility. Differential Revision: https://reviews.llvm.org/D140660
The LLVM Compiler Infrastructure
This directory and its sub-directories contain the 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 here.
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 convert them 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 frontend. 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 -
cmake -S llvm -B build -G <generator> [options]Some 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='...'and-DLLVM_ENABLE_RUNTIMES='...'--- semicolon-separated list of the LLVM sub-projects and runtimes you'd like to additionally build.LLVM_ENABLE_PROJECTScan include any of: clang, clang-tools-extra, cross-project-tests, flang, libc, libclc, lld, lldb, mlir, openmp, polly, or pstl.LLVM_ENABLE_RUNTIMEScan include any of libcxx, libcxxabi, libunwind, compiler-rt, libc or openmp. Some runtime projects can be specified either inLLVM_ENABLE_PROJECTSor inLLVM_ENABLE_RUNTIMES.For example, to build LLVM, Clang, libcxx, and libcxxabi, use
-DLLVM_ENABLE_PROJECTS="clang" -DLLVM_ENABLE_RUNTIMES="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). Be careful if you install runtime libraries: if your system uses those provided by LLVM (like libc++ or libc++abi), you must not overwrite your system's copy of those libraries, since that could render your system unusable. In general, using something like/usris not advised, but/usr/localis fine. -
-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 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 to run. In most cases, you get the best performance if you specify the number of CPU threads you have. On some Unix systems, you can specify this with-j$(nproc).
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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.
Getting in touch
Join LLVM Discourse forums, discord chat or #llvm IRC channel on OFTC.
The LLVM project has adopted a code of conduct for participants to all modes of communication within the project.