public static int Main(string[] args)
{
// First we'll declare some Vars to use below.
var x = new HSVar("x");
var y = new HSVar("y");
var xo = new HSVar("xo");
var yo = new HSVar("yo");
var xi = new HSVar("xi");
var yi = new HSVar("yi");
// This lesson will be about "wrapping" a Func or an ImageParam using the
// Func::in and ImageParam::in directives
{
{
// Consider a simple two-stage pipeline:
var f = new HSFunc("f_local");
var g = new HSFunc("g_local");
f[x, y] = x + y;
g[x, y] = 2 * f[x, y] + 3;
f.ComputeRoot();
// This produces the following loop nests:
// for y:
// for x:
// f(x, y) = x + y
// for y:
// for x:
// g(x, y) = 2 * f(x, y) + 3
// Using Func::in, we can interpose a new Func in between f
// and g using the schedule alone:
HSFunc f_in_g = f.In(g);
f_in_g.ComputeRoot();
// Equivalently, we could also chain the schedules like so:
// f.in(g).ComputeRoot();
// This produces the following three loop nests:
// for y:
// for x:
// f(x, y) = x + y
// for y:
// for x:
// f_in_g(x, y) = f(x, y)
// for y:
// for x:
// g(x, y) = 2 * f_in_g(x, y) + 3
g.Realize <int>(5, 5);
// See figures/lesson_19_wrapper_local.mp4 for a visualization.
}
// The schedule directive f.in(g) replaces all calls to 'f'
// inside 'g' with a wrapper Func and then returns that
// wrapper. Essentially, it rewrites the original pipeline
// above into the following:
{
var f_in_g = new HSFunc("f_in_g");
var f = new HSFunc("f");
var g = new HSFunc("g");
f[x, y] = x + y;
f_in_g[x, y] = f[x, y];
g[x, y] = 2 * f_in_g[x, y] + 3;
f.ComputeRoot();
f_in_g.ComputeRoot();
g.ComputeRoot();
}
// In isolation, such a transformation seems pointless, but it
// can be used for a variety of scheduling tricks.
}
{
// In the schedule above, only the calls to 'f' made by 'g'
// are replaced. Other calls made to f would still call 'f'
// directly. If we wish to globally replace all calls to 'f'
// with a single wrapper, we simply say f.in().
// Consider a three stage pipeline, with two consumers of f:
var f = new HSFunc("f_global");
var g = new HSFunc("g_global");
var h = new HSFunc("h_global");
f[x, y] = x + y;
g[x, y] = 2 * f[x, y];
h[x, y] = 3 + g[x, y] - f[x, y];
f.ComputeRoot();
g.ComputeRoot();
h.ComputeRoot();
// We will replace all calls to 'f' inside both 'g' and 'h'
// with calls to a single wrapper:
f.In().ComputeRoot();
// The equivalent loop nests are:
// for y:
// for x:
// f(x, y) = x + y
// for y:
// for x:
// f_in(x, y) = f(x, y)
// for y:
// for x:
// g(x, y) = 2 * f_in(x, y)
// for y:
// for x:
// h(x, y) = 3 + g(x, y) - f_in(x, y)
h.Realize <int>(5, 5);
// See figures/lesson_19_wrapper_global.mp4 and for a
// visualization of what this did.
}
{
// We could also give g and h their own unique wrappers of
// f. This time we'll schedule them each inside the loop nests
// of the consumer, which is not something we could do with a
// single global wrapper.
var f = new HSFunc("f_unique");
var g = new HSFunc("g_unique");
var h = new HSFunc("h_unique");
f[x, y] = x + y;
g[x, y] = 2 * f[x, y];
h[x, y] = 3 + g[x, y] - f[x, y];
f.ComputeRoot();
g.ComputeRoot();
h.ComputeRoot();
f.In(g).ComputeAt(g, y);
f.In(h).ComputeAt(h, y);
// This creates the loop nests:
// for y:
// for x:
// f(x, y) = x + y
// for y:
// for x:
// f_in_g(x, y) = f(x, y)
// for x:
// g(x, y) = 2 * f_in_g(x, y)
// for y:
// for x:
// f_in_h(x, y) = f(x, y)
// for x:
// h(x, y) = 3 + g(x, y) - f_in_h(x, y)
h.Realize <int>(5, 5);
// See figures/lesson_19_wrapper_unique.mp4 for a visualization.
}
{
// So far this may seem like a lot of pointless copying of
// memory. Func::in can be combined with other scheduling
// directives for a variety of purposes. The first we will
// examine is creating distinct realizations of a Func for
// several consumers and scheduling each differently.
// We'll start with nearly the same pipeline.
var f = new HSFunc("f_sched");
var g = new HSFunc("g_sched");
var h = new HSFunc("h_sched");
f[x, y] = x + y;
g[x, y] = 2 * f[x, y];
// h will use a far-away region of f
h[x, y] = 3 + g[x, y] - f[x + 93, y - 87];
// This time we'll inline f.
// f.ComputeRoot();
g.ComputeRoot();
h.ComputeRoot();
f.In(g).ComputeAt(g, y);
f.In(h).ComputeAt(h, y);
// g and h now call f via distinct wrappers. The wrappers are
// scheduled, but f is not, which means that f is inlined into
// its two wrappers. They will each independently compute the
// region of f required by their consumer. If we had scheduled
// f ComputeRoot, we'd be computing the bounding box of the
// region required by g and the region required by h, which
// would mostly be unused data.
// We can also schedule each of these wrappers
// differently. For scheduling purposes, wrappers inherit the
// pure vars of the Func they wrap, so we use the same x and y
// that we used when defining f:
f.In(g).Vectorize(x, 4);
f.In(h).Split(x, xo, xi, 2).Reorder(xo, xi);
// Note that calling f.in(g) a second time returns the wrapper
// already created by the first call, it doesn't make a new one.
h.Realize <int>(8, 8);
// See figures/lesson_19_wrapper_vary_schedule.mp4 for a
// visualization.
// Note that because f is inlined into its two wrappers, it is
// the wrappers that do the work of computing f, rather than
// just loading from an existing computed realization.
}
{
// Func::in is useful to stage loads from a Func via some
// smaller intermediate buffer, perhaps on the stack or in
// shared GPU memory.
// Consider a pipeline that transposes some ComputeRoot'd Func:
var f = new HSFunc("f_transpose");
var g = new HSFunc("g_transpose");
f[x, y] = HSMath.Sin(((x + y) * HSMath.Sqrt(y)) / 10);
f.ComputeRoot();
g[x, y] = f[y, x];
// The execution strategy we want is to load an 4x4 tile of f
// into registers, transpose it in-register, and then write it
// out as an 4x4 tile of g. We will use Func::in to express this:
HSFunc f_tile = f.In(g);
// We now have a three stage pipeline:
// f -> f_tile -> g
// f_tile will load vectors of f, and store them transposed
// into registers. g will then write this data back to main
// memory.
g.Tile(x, y, xo, yo, xi, yi, 4, 4)
.Vectorize(xi)
.Unroll(yi);
// We will compute f_transpose at tiles of g, and use
// Func::reorder_storage to state that f_transpose should be
// stored column-major, so that the loads to it done by g can
// be dense vector loads.
f_tile.ComputeAt(g, xo)
.ReorderStorage(y, x)
.Vectorize(x)
.Unroll(y);
// We take care to make sure f_transpose is only ever accessed
// at constant indicies. The full unrolling/vectorization of
// all loops that exist inside its compute_at level has this
// effect. Allocations that are only ever accessed at constant
// indices can be promoted into registers.
g.Realize <float>(16, 16);
// See figures/lesson_19_transpose.mp4 for a visualization
}
{
// ImageParam::in behaves the same way as Func::in, and you
// can use it to stage loads in similar ways. Instead of
// transposing again, we'll use ImageParam::in to stage tiles
// of an input image into GPU shared memory, effectively using
// shared/local memory as an explicitly-managed cache.
var img = new HSImageParam <int>(2);
// We will compute a small blur of the input.
var blur = new HSFunc("blur");
blur[x, y] = (img[x - 1, y - 1] + img[x, y - 1] + img[x + 1, y - 1] +
img[x - 1, y] + img[x, y] + img[x + 1, y] +
img[x - 1, y + 1] + img[x, y + 1] + img[x + 1, y + 1]);
blur.ComputeRoot().GpuTile(x, y, xo, yo, xi, yi, 8, 8);
// The wrapper Func created by ImageParam::in has pure vars
// named _0, _1, etc. Schedule it per tile of "blur", and map
// _0 and _1 to gpu threads.
img.In(blur).ComputeAt(blur, xo).GpuThreads(HS._0, HS._1);
// Without Func::in, computing an 8x8 tile of blur would do
// 8*8*9 loads to global memory. With Func::in, the wrapper
// does 10*10 loads to global memory up front, and then blur
// does 8*8*9 loads to shared/local memory.
// Select an appropriate GPU API, as we did in lesson 12
var target = HS.GetHostTarget();
if (target.OS == HSOperatingSystem.OSX)
{
target.SetFeature(HSFeature.Metal);
}
else
{
target.SetFeature(HSFeature.OpenCL);
}
// Create an interesting input image to use.
var input = new HSBuffer <int>(258, 258);
input.SetMin(-1, -1);
for (int yy = input.Top; yy <= input.Bottom; yy++)
{
for (int xx = input.Left; xx <= input.Right; xx++)
{
input[xx, yy] = xx * 17 + yy % 4;
}
}
img.Set(input);
blur.CompileJit(target);
var output = blur.Realize <int>(256, 256);
// Check the output is what we expected
for (int yy = output.Top; yy <= output.Bottom; yy++)
{
for (int xx = output.Left; xx <= output.Right; xx++)
{
int val = output[xx, yy];
int expected = (input[xx - 1, yy - 1] + input[xx, yy - 1] + input[xx + 1, yy - 1] +
input[xx - 1, yy] + input[xx, yy] + input[xx + 1, yy] +
input[xx - 1, yy + 1] + input[xx, yy + 1] + input[xx + 1, yy + 1]);
if (val != expected)
{
Console.WriteLine($"output({xx}, {yy}) = {val} instead of {expected}\n",
xx, yy, val, expected);
return(-1);
}
}
}
}
{
// Func::in can also be used to group multiple stages of a
// Func into the same loop nest. Consider the following
// pipeline, which computes a value per pixel, then sweeps
// from left to right and back across each scanline.
var f = new HSFunc("f_group");
var g = new HSFunc("g_group");
var h = new HSFunc("h_group");
// Initialize f
f[x, y] = HSMath.Sin(x - y);
var r = new HSRDom(1, 7);
// Sweep from left to right
f[r, y] = (f[r, y] + f[r - 1, y]) / 2;
// Sweep from right to left
f[7 - r, y] = (f[7 - r, y] + f[8 - r, y]) / 2;
// Then we do something with a complicated access pattern: A
// 45 degree rotation with wrap-around
g[x, y] = f[(x + y) % 8, (x - y) % 8];
// f should be scheduled ComputeRoot, because its consumer
// accesses it in a complicated way. But that means all stages
// of f are computed in separate loop nests:
// for y:
// for x:
// f(x, y) = sin(x - y)
// for y:
// for r:
// f(r, y) = (f(r, y) + f(r - 1, y)) / 2
// for y:
// for r:
// f(7 - r, y) = (f(7 - r, y) + f(8 - r, y)) / 2
// for y:
// for x:
// g(x, y) = f((x + y) % 8, (x - y) % 8);
// We can get better locality if we schedule the work done by
// f to share a common loop over y. We can do this by
// computing f at scanlines of a wrapper like so:
f.In(g).ComputeRoot();
f.ComputeAt(f.In(g), y);
// f has the default schedule for a Func with update stages,
// which is to be computed at the innermost loop of its
// consumer, which is now the wrapper f.in(g). This therefore
// generates the following loop nest, which has better
// locality:
// for y:
// for x:
// f(x, y) = sin(x - y)
// for r:
// f(r, y) = (f(r, y) + f(r - 1, y)) / 2
// for r:
// f(7 - r, y) = (f(7 - r, y) + f(8 - r, y)) / 2
// for x:
// f_in_g(x, y) = f(x, y)
// for y:
// for x:
// g(x, y) = f_in_g((x + y) % 8, (x - y) % 8);
// We'll additionally vectorize the initialization of, and
// then transfer of pixel values from f into its wrapper:
f.Vectorize(x, 4);
f.In(g).Vectorize(x, 4);
g.Realize <float>(8, 8);
// See figures/lesson_19_group_updates.mp4 for a visualization.
}
Console.WriteLine("Success!");
return(0);
}
internal HSImageParamDimensionList(HSImageParam <T> imageParam)
{
_imageParam = imageParam;
}
public static int Main(string[] argv)
{
// We'll define the simple one-stage pipeline that we used in lesson 10.
var brighter = new HSFunc("brighter");
var x = new HSVar("x");
var y = new HSVar("y");
// Declare the arguments.
var offset = new HSParam <byte>();
var input = new HSImageParam <byte>(2);
var args = new List <HSArgument>();
args.Add(input);
args.Add(offset);
// Define the Func.
brighter[x, y] = input[x, y] + offset;
// Schedule it.
brighter.Vectorize(x, 16).Parallel(y);
// The following line is what we did in lesson 10. It compiles an
// object file suitable for the system that you're running this
// program on. For example, if you compile and run this file on
// 64-bit linux on an x86 cpu with sse4.1, then the generated code
// will be suitable for 64-bit linux on x86 with sse4.1.
brighter.CompileToFile("lesson_11_host", args, "brighter");
// We can also compile object files suitable for other cpus and
// operating systems. You do this with an optional third argument
// to compile_to_file which specifies the target to compile for.
// Let's use this to compile a 32-bit arm android version of this code:
var target = new HSTarget();
target.OS = HSOperatingSystem.Android; // The operating system
target.Arch = HSArchitecture.ARM; // The CPU architecture
target.Bits = 32; // The bit-width of the architecture
var arm_features = new List <HSFeature>(); // A list of features to set
target.SetFeatures(arm_features);
// We then pass the target as the last argument to compile_to_file.
brighter.CompileToFile("lesson_11_arm_32_android", args, "brighter", target);
// And now a Windows object file for 64-bit x86 with AVX and SSE 4.1:
target.OS = HSOperatingSystem.Windows;
target.Arch = HSArchitecture.X86;
target.Bits = 64;
var x86_features = new List <HSFeature>();
x86_features.Add(HSFeature.AVX);
x86_features.Add(HSFeature.SSE41);
target.SetFeatures(x86_features);
brighter.CompileToFile("lesson_11_x86_64_windows", args, "brighter", target);
// And finally an iOS mach-o object file for one of Apple's 32-bit
// ARM processors - the A6. It's used in the iPhone 5. The A6 uses
// a slightly modified ARM architecture called ARMv7s. We specify
// this using the target features field. Support for Apple's
// 64-bit ARM processors is very new in llvm, and still somewhat
// flaky.
target.OS = HSOperatingSystem.IOS;
target.Arch = HSArchitecture.ARM;
target.Bits = 32;
var armv7s_features = new List <HSFeature>();
armv7s_features.Add(HSFeature.ARMv7s);
target.SetFeatures(armv7s_features);
brighter.CompileToFile("lesson_11_arm_32_ios", args, "brighter", target);
// Now let's check these files are what they claim, by examining
// their first few bytes.
{
// 32-arm android object files start with the magic bytes:
byte[] arm_32_android_magic = { 0x7f, (byte)'E', (byte)'L', (byte)'F', // ELF format
1, // 32-bit
1, // 2's complement little-endian
1 }; // Current version of elf
var androidObjectFile = "lesson_11_arm_32_android.o";
if (!File.Exists(androidObjectFile))
{
Console.WriteLine("Object file not generated");
return(-1);
}
var androidObjectData = File.ReadAllBytes(androidObjectFile);
var header = new byte[arm_32_android_magic.Length];
Buffer.BlockCopy(androidObjectData, 0, header, 0, arm_32_android_magic.Length);
if (!header.SequenceEqual(arm_32_android_magic))
{
Console.WriteLine("Unexpected header bytes in 32-bit arm object file.");
return(-1);
}
}
{
// 64-bit windows object files start with the magic 16-bit value 0x8664
// (presumably referring to x86-64)
byte[] win_64_magic = { 0x64, 0x86 };
var winObjectFile = "lesson_11_x86_64_windows.obj";
if (!File.Exists(winObjectFile))
{
Console.WriteLine("Object file not generated");
return(-1);
}
var windowsObjectData = File.ReadAllBytes(winObjectFile);
var header = new byte[win_64_magic.Length];
Buffer.BlockCopy(windowsObjectData, 0, header, 0, win_64_magic.Length);
if (!header.SequenceEqual(win_64_magic))
{
Console.WriteLine("Unexpected header bytes in 64-bit windows object file.");
return(-1);
}
}
{
// 32-bit arm iOS mach-o files start with the following magic bytes:
uint[] arm_32_ios_magic = { 0xfeedface, // Mach-o magic bytes
12, // CPU type is ARM
11, // CPU subtype is ARMv7s
1 }; // It's a relocatable object file.
var magicBytes = new byte[arm_32_ios_magic.Length * 4];
Buffer.BlockCopy(arm_32_ios_magic, 0, magicBytes, 0, arm_32_ios_magic.Length * 4);
var iosObjectFile = "lesson_11_arm_32_ios.o";
if (!File.Exists(iosObjectFile))
{
Console.WriteLine("Object file not generated");
return(-1);
}
var iosObjectData = File.ReadAllBytes(iosObjectFile);
var header = new byte[magicBytes.Length];
Buffer.BlockCopy(iosObjectData, 0, header, 0, magicBytes.Length);
if (!header.SequenceEqual(magicBytes))
{
Console.WriteLine("Unexpected header bytes in 32-bit arm ios object file.");
return(-1);
}
}
// It looks like the object files we produced are plausible for
// those targets. We'll count that as a success for the purposes
// of this tutorial. For a real application you'd then need to
// figure out how to integrate Halide into your cross-compilation
// toolchain. There are several small examples of this in the
// Halide repository under the apps folder. See HelloAndroid and
// HelloiOS here:
// https://github.com/halide/Halide/tree/master/apps/
Console.WriteLine("Success!");
return(0);
}
internal HSImageParamDimension(HSImageParam <T> imageParam, int d)
{
_imageParam = imageParam;
_dimension = d;
}
