c8d2b065b9
Changes: * Change `ToVectorTy` to deal directly with `ElementCount` instances. * `VF == 1` replaced with `VF.isScalar()`. * `VF > 1` and `VF >=2` replaced with `VF.isVector()`. * `VF <=1` is replaced with `VF.isZero() || VF.isScalar()`. * Add `<` operator to `ElementCount` to be able to use `llvm::SmallSetVector<ElementCount, ...>`. * Bits and pieces around printing the ElementCount to string streams. * Added a static method to `ElementCount` to represent a scalar. To guarantee that this change is a NFC, `VF.Min` and asserts are used in the following places: 1. When it doesn't make sense to deal with the scalable property, for example: a. When computing unrolling factors. b. When shuffle masks are built for fixed width vector types In this cases, an assert(!VF.Scalable && "<mgs>") has been added to make sure we don't enter coepaths that don't make sense for scalable vectors. 2. When there is a conscious decision to use `FixedVectorType`. These uses of `FixedVectorType` will likely be removed in favour of `VectorType` once the vectorizer is generic enough to deal with both fixed vector types and scalable vector types. 3. When dealing with building constants out of the value of VF, for example when computing the vectorization `step`, or building vectors of indices. These operation _make sense_ for scalable vectors too, but changing the code in these places to be generic and make it work for scalable vectors is to be submitted in a separate patch, as it is a functional change. 4. When building the potential VFs in VPlan. Making the VPlan generic enough to handle scalable vectorization factors is a functional change that needs a separate patch. See for example `void LoopVectorizationPlanner::buildVPlans(unsigned MinVF, unsigned MaxVF)`. 5. The class `IntrinsicCostAttribute`: this class still uses `unsigned VF` as updating the field to use `ElementCount` woudl require changes that could result in changing the behavior of the compiler. Will be done in a separate patch. 7. When dealing with user input for forcing the vectorization factor. In this case, adding support for scalable vectorization is a functional change that migh require changes at command line. Differential Revision: https://reviews.llvm.org/D85794
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.