commit | ae1ea0bead75f4c7a4c965dfa40b5f3b78b60364 | [log] [tgz] |
---|---|---|
author | Julian Gross <julian.gross@dfki.de> | Thu Nov 18 16:44:46 2021 +0100 |
committer | Julian Gross <julian.gross@dfki.de> | Tue Nov 30 10:15:56 2021 +0100 |
tree | 0b0478322d0e53d51056dce1125e843d0983d181 | |
parent | 3356d8837e46a92446e4b9b0cbd6967e5f4e44ba [diff] |
[mlir] Decompose Bufferization Clone operation into Memref Alloc and Copy. This patch introduces a new conversion to convert bufferization.clone operations into a memref.alloc and a memref.copy operation. This transformation is needed to transform all remaining clones which "survive" all previous transformations, before a given program is lowered further (to LLVM e.g.). Otherwise, these operations cannot be handled anymore and lead to compile errors. See: https://llvm.discourse.group/t/bufferization-error-related-to-memref-clone/4665 Differential Revision: https://reviews.llvm.org/D114233
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.
Taken from https://llvm.org/docs/GettingStarted.html.
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 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.
The LLVM Getting Started documentation may be out of date.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:
Checkout LLVM (including related sub-projects like Clang):
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
Configure and build LLVM and Clang:
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:
-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, compiler-rt,cross-project-tests, flang, libc, libclc, libcxx, libcxxabi, libunwind, lld, lldb, mlir, openmp, polly, or pstl.
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).
cmake --build build [-- [options] <target>]
or your build system specified above directly.
The default target (i.e. ninja
or make
) will build all of LLVM.
The check-all
target (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
, where NNN
is the number of parallel jobs, e.g. the number of CPUs you have.
For more information see CMake
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.