Nirvedh Meshram 42b3f91fd6
[mlir] Vectorize tensor.pad with low padding for unit dims (#133808)
We currently do not have masked vectorization support for tenor.pad with
low padding. However, we can allow this in the special case where the
result dimension after padding is a unit dim. The reason is when we
actually have a low pad on a unit dim, the input size of that dimension
will be (or should be for correct IR) dynamically zero and hence we will
create a zero mask which is correct. If the low pad is dynamically zero
then the lowering is correct as well.

---------

Signed-off-by: Nirvedh <nirvedh@gmail.com>
2025-04-02 16:32:36 -05:00
2025-01-28 19:48:43 -08:00
2025-02-13 17:49:48 +00:00

The LLVM Compiler Infrastructure

OpenSSF Scorecard OpenSSF Best Practices libc++

Welcome to the LLVM project!

This repository contains the source code for LLVM, a toolkit for the construction of highly optimized compilers, optimizers, and run-time environments.

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.

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

Consult the Getting Started with LLVM page for information on building and running LLVM.

For information on how to contribute to the LLVM project, please take a look at the Contributing to LLVM guide.

Getting in touch

Join the LLVM Discourse forums, Discord chat, LLVM Office Hours or Regular sync-ups.

The LLVM project has adopted a code of conduct for participants to all modes of communication within the project.

Description
The LLVM Project is a collection of modular and reusable compiler and toolchain technologies.
Readme 5 GiB
Languages
LLVM 39.9%
C++ 32.5%
C 13.5%
Assembly 9.4%
MLIR 1.4%
Other 2.8%