Halide

Halide is a programming language designed to make it easier to write high-performance image and array processing code on modern machines. Halide currently targets:

CPU architectures: X86, ARM, MIPS, Hexagon, PowerPC
Operating systems: Linux, Windows, Mac OS X, Android, iOS, Qualcomm QuRT
GPU Compute APIs: CUDA, OpenCL, OpenGL, OpenGL Compute Shaders, Apple Metal, Microsoft Direct X 12

Rather than being a standalone programming language, Halide is embedded in C++. This means you write C++ code that builds an in-memory representation of a Halide pipeline using Halide's C++ API. You can then compile this representation to an object file, or JIT-compile it and run it in the same process. Halide also provides a Python binding that provides full support for writing Halide embedded in Python without C++.

For more detail about what Halide is, see http://halide-lang.org.

For API documentation see http://halide-lang.org/docs

To see some example code, look in the tutorials directory.

If you've acquired a full source distribution and want to build Halide, see the notes below.

Building Halide with Make

TL;DR

Have llvm-9.0 (or greater) installed and run make in the root directory of the repository (where this README is).

Acquiring LLVM

At any point in time, building Halide requires either the latest stable version of LLVM, the previous stable version of LLVM, and trunk. At the time of writing, this means versions 10.0 and 9.0 are supported, but 8.0 is not. The commands llvm-config and clang must be somewhere in the path.

If your OS does not have packages for llvm, you can find binaries for it at http://llvm.org/releases/download.html. Download an appropriate package and then either install it, or at least put the bin subdirectory in your path. (This works well on OS X and Ubuntu.)

If you want to build it yourself, first check it out from GitHub:

% git clone --depth 1 --branch llvmorg-10.0.0 https://github.com/llvm/llvm-project.git

(If you want to build LLVM 9.x, use branch release/9.x; for current trunk, use master)

Then build it like so:

% mkdir llvm-build
% cd llvm-build
% cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=../llvm-install \
        -DLLVM_ENABLE_PROJECTS="clang;lld;clang-tools-extra" \
        -DLLVM_TARGETS_TO_BUILD="X86;ARM;NVPTX;AArch64;Mips;Hexagon" \
        -DLLVM_ENABLE_TERMINFO=OFF -DLLVM_ENABLE_ASSERTIONS=ON \
        -DLLVM_ENABLE_EH=ON -DLLVM_ENABLE_RTTI=ON -DLLVM_BUILD_32_BITS=OFF \
        ../llvm-project/llvm
% cmake --build . --target install

then to point Halide to it:

export LLVM_CONFIG=<path to llvm>/llvm-install/bin/llvm-config

Note that you must add clang to LLVM_ENABLE_PROJECTS; adding lld to LLVM_ENABLE_PROJECTS is only required when using WebAssembly, and adding clang-tools-extra is only necessary if you plan to contribute code to Halide (so that you can run clang-tidy on your pull requests). We recommend enabling both in all cases, to simplify builds. You can disable exception handling (EH) and RTTI if you don't want the Python bindings.

Building Halide with make

With LLVM_CONFIG set (or llvm-config in your path), you should be able to just run make in the root directory of the Halide source tree. make run_tests will run the JIT test suite, and make test_apps will make sure all the apps compile and run (but won't check their output).

There is no make install yet. If you want to make an install package, run make distrib.

Building Halide out-of-tree with make

If you wish to build Halide in a separate directory, you can do that like so:

% cd ..
% mkdir halide_build
% cd halide_build
% make -f ../Halide/Makefile

Building Halide with CMake

MacOS and Linux

Follow the above instructions to build LLVM or acquire a suitable binary release. Then create a separate build folder for Halide and run CMake, pointing it to your LLVM installation.

% mkdir Halide-build
% cd Halide-build
% cmake -DCMAKE_BUILD_TYPE=Release -DLLVM_DIR=/path/to/llvm-install/lib/cmake/llvm /path/to/Halide
% cmake --build .

LLVM_DIR should be the folder in the LLVM installation or build tree that contains LLVMConfig.cmake. It is not required if you have a suitable system-wide version installed. If you have multiple system-wide versions installed, you can specify the version with HALIDE_REQUIRE_LLVM_VERSION. Add -G Ninja if you prefer to build with the Ninja generator.

Windows

We recommend building with MSVC 2019, but MSVC 2017 is also supported. Be sure to install the CMake Individual Component in the Visual Studio 2019 installer. For older versions of Visual Studio, do not install the CMake tools, but instead acquire CMake and Ninja from their respective project websites.

These instructions start from the D: drive. We assume this git repo is cloned to D:\Halide. We also assume that your shell environment is set up correctly. For a 64-bit build, run:

D:\> "C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Auxiliary\Build\vcvarsall.bat" x64

For a 32-bit build, run:

D:\> "C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Auxiliary\Build\vcvarsall.bat" x86_amd64

Managing dependencies with vcpkg

The best way to get compatible dependencies on Windows is to use vcpkg. Install it like so:

D:\> git clone https://github.com/Microsoft/vcpkg.git
D:\> cd vcpkg
D:\> .\bootstrap-vcpkg.bat
D:\vcpkg> .\vcpkg integrate install
...
CMake projects should use: "-DCMAKE_TOOLCHAIN_FILE=D:/vcpkg/scripts/buildsystems/vcpkg.cmake"

Then install the libraries. For a 64-bit build, run:

D:\vcpkg> .\vcpkg install libpng:x64-windows libjpeg-turbo:x64-windows llvm[target-all,clang-tools-extra]:x64-windows

To support 32-bit builds, also run:

D:\vcpkg> .\vcpkg install libpng:x86-windows libjpeg-turbo:x86-windows llvm[target-all,clang-tools-extra]:x86-windows

Building Halide

Create a separate build tree and call CMake with vcpkg's toolchain. This will build in either 32-bit or 64-bit depending on the environment script (vcvars) that was run earlier.

D:\> md Halide-build
D:\> cd Halide-build
D:\Halide-build> cmake -G Ninja ^
                       -DCMAKE_BUILD_TYPE=Release ^
                       -DCMAKE_TOOLCHAIN_FILE=D:/vcpkg/scripts/buildsystems/vcpkg.cmake ^
                       ..\Halide

Note: If building with Python bindings on 32-bit (enabled by default), be sure to point CMake to the installation path of a 32-bit Python 3. You can do this by specifying, for example: "-DPython3_ROOT_DIR=C:\Program Files (x86)\Python38-32".

Then run the build with:

D:\Halide-build> cmake --build . --config Release -j %NUMBER_OF_PROCESSORS%

To run all the tests:

D:\Halide-build> ctest -C Release

Subsets of the tests can be selected with -L and include correctness, python, error, and the other directory names under /tests.

Building LLVM (optional)

Follow these steps if you want to build LLVM yourself. First, download LLVM's sources (these instructions use the latest 10.0 release)

D:\> git clone --depth 1 --branch llvmorg-10.0.0 https://github.com/llvm/llvm-project.git

For a 64-bit build, run:

D:\> md llvm-build
D:\> cd llvm-build
D:\llvm-build> cmake -G Ninja ^
                     -DCMAKE_BUILD_TYPE=Release ^
                     -DCMAKE_INSTALL_PREFIX=../llvm-install ^
                     -DLLVM_ENABLE_PROJECTS=clang;lld;clang-tools-extra ^
                     -DLLVM_ENABLE_TERMINFO=OFF ^
                     -DLLVM_TARGETS_TO_BUILD=X86;ARM;NVPTX;AArch64;Mips;Hexagon ^
                     -DLLVM_ENABLE_ASSERTIONS=ON ^
                     -DLLVM_ENABLE_EH=ON ^
                     -DLLVM_ENABLE_RTTI=ON ^
                     -DLLVM_BUILD_32_BITS=OFF ^
                     ..\llvm-project\llvm

For a 32-bit build, run:

D:\> md llvm32-build
D:\> cd llvm32-build
D:\llvm32-build> cmake -G Ninja ^
                       -DCMAKE_BUILD_TYPE=Release ^
                       -DCMAKE_INSTALL_PREFIX=../llvm32-install ^
                       -DLLVM_ENABLE_PROJECTS=clang;lld;clang-tools-extra ^
                       -DLLVM_ENABLE_TERMINFO=OFF ^
                       -DLLVM_TARGETS_TO_BUILD=X86;ARM;NVPTX;AArch64;Mips;Hexagon ^
                       -DLLVM_ENABLE_ASSERTIONS=ON ^
                       -DLLVM_ENABLE_EH=ON ^
                       -DLLVM_ENABLE_RTTI=ON ^
                       -DLLVM_BUILD_32_BITS=ON ^
                       ..\llvm-project\llvm

Finally, run:

D:\llvm-build> cmake --build . --config Release --target install -j %NUMBER_OF_PROCESSORS%

You can substitute Debug for Release in the above cmake commands if you want a debug build. Make sure to add -DLLVM_DIR=D:/llvm-install/lib/cmake/llvm to the Halide CMake command to override vcpkg's LLVM.

MSBuild: If you want to build LLVM with MSBuild instead of Ninja, use -G "Visual Studio 16 2019" -Thost=x64 -A x64 or -G "Visual Studio 16 2019" -Thost=x64 -A Win32 in place of -G Ninja.

If all else fails...

Do what the build-bots do: https://buildbot.halide-lang.org/master/#/builders

If the column that best matches your system is red, then maybe things aren't just broken for you. If it's green, then you can click the "stdio" links in the latest build to see what commands the build bots run, and what the output was.

Some useful environment variables

HL_TARGET=... will set Halide's AOT compilation target.

HL_JIT_TARGET=... will set Halide's JIT compilation target.

HL_DEBUG_CODEGEN=1 will print out pseudocode for what Halide is compiling. Higher numbers will print more detail.

HL_NUM_THREADS=... specifies the number of threads to create for the thread pool. When the async scheduling directive is used, more threads than this number may be required and thus allocated. A maximum of 256 threads is allowed. (By default, the number of cores on the host is used.)

HL_TRACE_FILE=... specifies a binary target file to dump tracing data into (ignored unless at least one trace_ feature is enabled in HL_TARGET or HL_JIT_TARGET). The output can be parsed programmatically by starting from the code in utils/HalideTraceViz.cpp.

Using Halide on OSX

Precompiled Halide distributions are built using XCode's command-line tools with Apple clang 500.2.76. This means that we link against libc++ instead of libstdc++. You may need to adjust compiler options accordingly if you're using an older XCode which does not default to libc++.

Halide OpenGL/GLSL backend

Halide's OpenGL backend offloads image processing operations to the GPU by generating GLSL-based fragment shaders.

Compared to other GPU-based processing options such as CUDA and OpenCL, OpenGL has two main advantages: it is available on basically every desktop computer and mobile device, and it is generally well supported across different hardware vendors.

The main disadvantage of OpenGL as an image processing framework is that the computational capabilities of fragment shaders are quite restricted. In general, the processing model provided by OpenGL is most suitable for filters where each output pixel can be expressed as a simple function of the input pixels. This covers a wide range of interesting operations like point-wise filters and convolutions; but a few common image processing operations such as histograms or recursive filters are notoriously hard to express in GLSL.

Writing OpenGL-Based Filters

To enable code generation for OpenGL, include opengl in the target specifier passed to Halide. Since OpenGL shaders are limited in their computational power, you must also specify a CPU target for those parts of the filter that cannot or should not be computed on the GPU. Examples of valid target specifiers are

host-opengl
x86-opengl-debug

Adding debug, as in the second example, adds additional logging output and is highly recommended during development.

By default, filters compiled for OpenGL targets run completely on the CPU. Execution on the GPU must be enabled for individual Funcs by appropriate scheduling calls.

GLSL fragment shaders implicitly iterate over two spatial dimensions x,y and the color channel. Due to the way color channels handled in GLSL, only filters for which the color index is a compile-time constant can be scheduled. The main consequence is that the range of color variables must be explicitly specified for both input and output buffers before scheduling:

ImageParam input;
Func f;
Var x, y, c;
f(x, y, c) = ...;

input.set_bounds(2, 0, 3);   // specify color range for input
f.bound(c, 0, 3);            // and output
f.glsl(x, y, c);

JIT Compilation

For JIT compilation Halide attempts to load the system libraries for opengl and creates a new context to use for each module. Windows is not yet supported.

Examples for JIT execution of OpenGL-based filters can be found in test/opengl.

AOT Compilation

When AOT (ahead-of-time) compilation is used, Halide generates OpenGL-enabled object files that can be linked to and called from a host application. In general, this is fairly straightforward, but a few things must be taken care of.

On Linux, OS X, and Android, Halide creates its own OpenGL context unless the current thread already has an active context. On other platforms you have to link implementations of the following two functions with your Halide code:

extern "C" int halide_opengl_create_context(void *) {
    return 0;  // if successful
}

extern "C" void *halide_opengl_get_proc_addr(void *, const char *name) {
    ...
}

Halide allocates and deletes textures as necessary. Applications may manage the textures by hand by setting the halide_buffer_t::device field; this is most useful for reusing image data that is already stored in textures. Some rudimentary checks are performed to ensure that externally allocated textures have the correct format, but in general that's the responsibility of the application.

It is possible to let render directly to the current framebuffer; to do this, set the dev field of the output buffer to the value returned by halide_opengl_output_client_bound. The example in apps/HelloAndroidGL demonstrates this technique.

Some operating systems can delete the OpenGL context of suspended applications. If this happens, Halide needs to re-initialize itself with the new context after the application resumes. Call halide_opengl_context_lost to reset Halide's OpenGL state after this has happened.

Limitations

The current implementation of the OpenGL backend targets the common subset of OpenGL 2.0 and OpenGL ES 2.0 which is widely available on both mobile devices and traditional computers. As a consequence, only a subset of the Halide language can be scheduled to run using OpenGL. Some important limitations are:

Reductions cannot be implemented in GLSL and must be run on the CPU.
OpenGL ES 2.0 only supports uint8 buffers.

Support for floating point texture is available, but requires OpenGL (ES) 3.0 or the texture_float extension, which may not work on all mobile devices.
OpenGL ES 2.0 has very limited support for integer arithmetic. For maximum compatibility, consider doing all computations using floating point, even when using integer textures.
Only 2D images with 3 or 4 color channels can be scheduled. Images with one or two channels require OpenGL (ES) 3.0 or the texture_rg extension.
Not all builtin functions provided by Halide are currently supported, for example fast_log, fast_exp, fast_pow, reinterpret, bit operations, random_float, random_int cannot be used in GLSL code.

The maximum texture size in OpenGL is GL_MAX_TEXTURE_SIZE, which is often smaller than the image of interest; on mobile devices, for example, GL_MAX_TEXTURE_SIZE is commonly 2048. Tiling must be used to process larger images.

Planned features:

Support for half-float textures and arithmetic
Support for integer textures and arithmetic

(Note that OpenGL Compute Shaders are supported with a separate OpenGLCompute backend.)

Halide for Hexagon HVX

Halide supports offloading work to Qualcomm Hexagon DSP on Qualcomm Snapdragon 820 devices or newer. The Hexagon DSP provides a set of 64 and 128 byte vector instructions - the Hexagon Vector eXtensions (HVX). HVX is well suited to image processing, and Halide for Hexagon HVX will generate the appropriate HVX vector instructions from a program authored in Halide.

Halide can be used to compile Hexagon object files directly, by using a target such as hexagon-32-qurt-hvx_64 or hexagon-32-qurt-hvx_128.

Halide can also be used to offload parts of a pipeline to Hexagon using the hexagon scheduling directive. To enable the hexagon scheduling directive, include the hvx_64 or hvx_128 target features in your target. The currently supported combination of targets is to use the HVX target features with an x86 linux host (to use the simulator) or with an ARM android target (to use Hexagon DSP hardware). For examples of using the hexagon scheduling directive on both the simulator and a Hexagon DSP, see the blur example app.

To build and run an example app using the Hexagon target,

Obtain and build trunk LLVM and Clang. (Earlier versions of LLVM may work but are not actively tested and thus not recommended.)
Download and install the Hexagon SDK and version 8.0 Hexagon Tools
Build and run an example for Hexagon HVX

1. Obtain and build trunk LLVM and Clang

(Instructions given previous, just be sure to check out the master branch.)

2. Download and install the Hexagon SDK and version 8.0 Hexagon Tools

Go to https://developer.qualcomm.com/software/hexagon-dsp-sdk/tools

Select the Hexagon Series 600 Software and download the 3.0 version for Linux.
untar the installer
Run the extracted installer to install the Hexagon SDK and Hexagon Tools, selecting Installation of Hexagon SDK into /location/of/SDK/Hexagon_SDK/3.0 and the Hexagon tools into /location/of/SDK/Hexagon_Tools/8.0
Set an environment variable to point to the SDK installation location
```
export SDK_LOC=/location/of/SDK
```

3. Build and run an example for Hexagon HVX

In addition to running Hexagon code on device, Halide also supports running Hexagon code on the simulator from the Hexagon tools.

To build and run the blur example in Halide/apps/blur on the simulator:

cd apps/blur
export HL_HEXAGON_SIM_REMOTE=../../src/runtime/hexagon_remote/bin/v60/hexagon_sim_remote
export HL_HEXAGON_TOOLS=$SDK_LOC/Hexagon_Tools/8.0/Tools/
LD_LIBRARY_PATH=../../src/runtime/hexagon_remote/bin/host/:$HL_HEXAGON_TOOLS/lib/iss/:. HL_TARGET=host-hvx_128 make test

To build and run the blur example in Halide/apps/blur on Android:

To build the example for Android, first ensure that you have a standalone toolchain created from the NDK using the make-standalone-toolchain.sh script:

export ANDROID_NDK_HOME=$SDK_LOC/Hexagon_SDK/3.0/tools/android-ndk-r10d/
export ANDROID_ARM64_TOOLCHAIN=<path to put new arm64 toolchain>
$ANDROID_NDK_HOME/build/tools/make-standalone-toolchain.sh --arch=arm64 --platform=android-21 \
    --install-dir=$ANDROID_ARM64_TOOLCHAIN

Now build and run the blur example using the script to run it on device:

export HL_HEXAGON_TOOLS=$SDK_LOC/HEXAGON_Tools/8.0/Tools/
HL_TARGET=arm-64-android-hvx_128 ./adb_run_on_device.sh

NewProggie / Halide