ee66e43a96
This patch adds the 'vector.load' and 'vector.store' ops to the Vector dialect [1]. These operations model *contiguous* vector loads and stores from/to memory. Their semantics are similar to the 'affine.vector_load' and 'affine.vector_store' counterparts but without the affine constraints. The most relevant feature is that these new vector operations may perform a vector load/store on memrefs with a non-vector element type, unlike 'std.load' and 'std.store' ops. This opens the representation to model more generic vector load/store scenarios: unaligned vector loads/stores, perform scalar and vector memory access on the same memref, decouple memory allocation constraints from memory accesses, etc [1]. These operations will also facilitate the progressive lowering of both Affine vector loads/stores and Vector transfer reads/writes for those that read/write contiguous slices from/to memory. In particular, this patch adds the 'vector.load' and 'vector.store' ops to the Vector dialect, implements their lowering to the LLVM dialect, and changes the lowering of 'affine.vector_load' and 'affine.vector_store' ops to the new vector ops. The lowering of Vector transfer reads/writes will be implemented in the future, probably as an independent pass. The API of 'vector.maskedload' and 'vector.maskedstore' has also been changed slightly to align it with the transfer read/write ops and the vector new ops. This will improve reusability among all these operations. For example, the lowering of 'vector.load', 'vector.store', 'vector.maskedload' and 'vector.maskedstore' to the LLVM dialect is implemented with a single template conversion pattern. [1] https://llvm.discourse.group/t/memref-type-and-data-layout/ Reviewed By: nicolasvasilache Differential Revision: https://reviews.llvm.org/D96185 |
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| .github | ||
| clang | ||
| clang-tools-extra | ||
| compiler-rt | ||
| debuginfo-tests | ||
| flang | ||
| libc | ||
| libclc | ||
| libcxx | ||
| libcxxabi | ||
| libunwind | ||
| lld | ||
| lldb | ||
| llvm | ||
| mlir | ||
| openmp | ||
| parallel-libs | ||
| polly | ||
| pstl | ||
| runtimes | ||
| utils/arcanist | ||
| .arcconfig | ||
| .arclint | ||
| .clang-format | ||
| .clang-tidy | ||
| .git-blame-ignore-revs | ||
| .gitignore | ||
| CONTRIBUTING.md | ||
| README.md | ||
README.md
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.