// Now we define methods that give our pipeline several different
// schedules.
public void ScheduleForCpu()
{
// Compute the look-up-table ahead of time.
Lut.ComputeRoot();
// Compute color channels innermost. Promise that there will
// be three of them and unroll across them.
Curved.Reorder(C, X, Y)
.Bound(C, 0, 3)
.Unroll(C);
// Look-up-tables don't vectorize well, so just parallelize
// curved in slices of 16 scanlines.
var yo = new HSVar("yo");
var yi = new HSVar("yi");
Curved.Split(Y, yo, yi, 16)
.Parallel(yo);
// Compute sharpen as needed per scanline of curved.
Sharpen.ComputeAt(Curved, yi);
// Vectorize the sharpen. It's 16-bit so we'll vectorize it 8-wide.
Sharpen.Vectorize(X, 8);
// Compute the padded input as needed per scanline of curved,
// reusing previous values computed within the same strip of
// 16 scanlines.
Padded.StoreAt(Curved, yo)
.ComputeAt(Curved, yi);
// Also vectorize the padding. It's 8-bit, so we'll vectorize
// 16-wide.
Padded.Vectorize(X, 16);
// JIT-compile the pipeline for the CPU.
Curved.CompileJit();
}
public static int Main(string[] args)
{
// We're going to define and schedule our gradient function in
// several different ways, and see what order pixels are computed
// in.
var x = new HSVar("x");
var y = new HSVar("y");
// First we observe the default ordering.
{
var gradient = new HSFunc("gradient");
gradient[x, y] = x + y;
gradient.TraceStores();
// By default we walk along the rows and then down the
// columns. This means x varies quickly, and y varies
// slowly. x is the column and y is the row, so this is a
// row-major traversal.
Console.WriteLine("Evaluating gradient row-major");
var output = gradient.Realize <int>(4, 4);
// See figures/lesson_05_row_major.gif for a visualization of
// what this did.
// The equivalent C is:
Console.WriteLine("Equivalent C:");
for (int yy = 0; yy < 4; yy++)
{
for (int xx = 0; xx < 4; xx++)
{
Console.WriteLine($"Evaluating at x = {xx}, y = {yy}: {xx + yy}");
}
}
Console.WriteLine("\n");
// Tracing is one useful way to understand what a schedule is
// doing. You can also ask Halide to print out pseudocode
// showing what loops Halide is generating:
Console.WriteLine("Pseudo-code for the schedule:");
gradient.PrintLoopNest();
Console.WriteLine();
// Because we're using the default ordering, it should print:
// compute gradient:
// for y:
// for x:
// gradient(...) = ...
}
// Reorder variables.
{
var gradient = new HSFunc("gradient_col_major");
gradient[x, y] = x + y;
gradient.TraceStores();
// If we reorder x and y, we can walk down the columns
// instead. The reorder call takes the arguments of the func,
// and sets a new nesting order for the for loops that are
// generated. The arguments are specified from the innermost
// loop out, so the following call puts y in the inner loop:
gradient.Reorder(y, x);
// This means y (the row) will vary quickly, and x (the
// column) will vary slowly, so this is a column-major
// traversal.
Console.WriteLine("Evaluating gradient column-major");
var output = gradient.Realize <int>(4, 4);
// See figures/lesson_05_col_major.gif for a visualization of
// what this did.
Console.WriteLine("Equivalent C:");
for (int xx = 0; xx < 4; xx++)
{
for (int yy = 0; yy < 4; yy++)
{
Console.WriteLine($"Evaluating at x = {xx}, y = {yy}: {xx + yy}");
}
}
Console.WriteLine();
// If we print pseudo-code for this schedule, we'll see that
// the loop over y is now inside the loop over x.
Console.WriteLine("Pseudo-code for the schedule:");
gradient.PrintLoopNest();
Console.WriteLine();
}
// Split a variable into two.
{
var gradient = new HSFunc("gradient_split");
gradient[x, y] = x + y;
gradient.TraceStores();
// The most powerful primitive scheduling operation you can do
// to a var is to split it into inner and outer sub-variables:
var x_outer = new HSVar("x_outer");
var x_inner = new HSVar("x_inner");
gradient.Split(x, x_outer, x_inner, 2);
// This breaks the loop over x into two nested loops: an outer
// one over x_outer, and an inner one over x_inner. The last
// argument to split was the "split factor". The inner loop
// runs from zero to the split factor. The outer loop runs
// from zero to the extent required of x (4 in this case)
// divided by the split factor. Within the loops, the old
// variable is defined to be outer * factor + inner. If the
// old loop started at a value other than zero, then that is
// also added within the loops.
Console.WriteLine("Evaluating gradient with x split into x_outer and x_inner ");
var output = gradient.Realize <int>(4, 4);
Console.WriteLine("Equivalent C:");
for (int yy = 0; yy < 4; yy++)
{
for (int xOuter = 0; xOuter < 2; xOuter++)
{
for (int xInner = 0; xInner < 2; xInner++)
{
int xx = xOuter * 2 + xInner;
Console.WriteLine($"Evaluating at x = {xx}, y = {yy}: {xx + yy}");
}
}
}
Console.WriteLine();
Console.WriteLine("Pseudo-code for the schedule:");
gradient.PrintLoopNest();
Console.WriteLine();
// Note that the order of evaluation of pixels didn't actually
// change! Splitting by itself does nothing, but it does open
// up all of the scheduling possibilities that we will explore
// below.
}
// Fuse two variables into one.
{
var gradient = new HSFunc("gradient_fused");
gradient[x, y] = x + y;
// The opposite of splitting is 'fusing'. Fusing two variables
// merges the two loops into a single for loop over the
// product of the extents. Fusing is less important than
// splitting, but it also sees use (as we'll see later in this
// lesson). Like splitting, fusing by itself doesn't change
// the order of evaluation.
var fused = new HSVar("fused");
gradient.Fuse(x, y, fused);
Console.WriteLine("Evaluating gradient with x and y fused");
var output = gradient.Realize <int>(4, 4);
Console.WriteLine("Equivalent C:");
for (int f = 0; f < 4 * 4; f++)
{
int yy = f / 4;
int xx = f % 4;
Console.WriteLine($"Evaluating at x = {xx}, y = {yy}: {xx + yy}");
}
Console.WriteLine();
Console.WriteLine("Pseudo-code for the schedule:");
gradient.PrintLoopNest();
Console.WriteLine();
}
// Evaluating in tiles.
{
var gradient = new HSFunc("gradient_tiled");
gradient[x, y] = x + y;
gradient.TraceStores();
// Now that we can both split and reorder, we can do tiled
// evaluation. Let's split both x and y by a factor of four,
// and then reorder the vars to express a tiled traversal.
//
// A tiled traversal splits the domain into small rectangular
// tiles, and outermost iterates over the tiles, and within
// that iterates over the points within each tile. It can be
// good for performance if neighboring pixels use overlapping
// input data, for example in a blur. We can express a tiled
// traversal like so:
var x_outer = new HSVar("x_outer");
var x_inner = new HSVar("x_inner");
var y_outer = new HSVar("y_outer");
var y_inner = new HSVar("y_inner");
gradient.Split(x, x_outer, x_inner, 4);
gradient.Split(y, y_outer, y_inner, 4);
gradient.Reorder(x_inner, y_inner, x_outer, y_outer);
// This pattern is common enough that there's a shorthand for it:
// gradient.tile(x, y, x_outer, y_outer, x_inner, y_inner, 4, 4);
Console.WriteLine("Evaluating gradient in 4x4 tiles");
var output = gradient.Realize <int>(8, 8);
// See figures/lesson_05_tiled.gif for a visualization of this
// schedule.
Console.WriteLine("Equivalent C:");
for (int yOuter = 0; yOuter < 2; yOuter++)
{
for (int xOuter = 0; xOuter < 2; xOuter++)
{
for (int yInner = 0; yInner < 4; yInner++)
{
for (int xInner = 0; xInner < 4; xInner++)
{
int xx = xOuter * 4 + xInner;
int yy = yOuter * 4 + yInner;
Console.WriteLine($"Evaluating at x = {xx}, y = {yy}: {xx + yy}");
}
}
}
}
Console.WriteLine();
Console.WriteLine("Pseudo-code for the schedule:");
gradient.PrintLoopNest();
Console.WriteLine();
}
// Evaluating in vectors.
{
var gradient = new HSFunc("gradient_in_vectors");
gradient[x, y] = x + y;
gradient.TraceStores();
// The nice thing about splitting is that it guarantees the
// inner variable runs from zero to the split factor. Most of
// the time the split-factor will be a compile-time constant,
// so we can replace the loop over the inner variable with a
// single vectorized computation. This time we'll split by a
// factor of four, because on X86 we can use SSE to compute in
// 4-wide vectors.
var x_outer = new HSVar("x_outer");
var x_inner = new HSVar("x_inner");
gradient.Split(x, x_outer, x_inner, 4);
gradient.Vectorize(x_inner);
// Splitting and then vectorizing the inner variable is common
// enough that there's a short-hand for it. We could have also
// said:
//
// gradient.vectorize(x, 4);
//
// which is equivalent to:
//
// gradient.split(x, x, x_inner, 4);
// gradient.vectorize(x_inner);
//
// Note that in this case we reused the name 'x' as the new
// outer variable. Later scheduling calls that refer to x
// will refer to this new outer variable named x.
// This time we'll evaluate over an 8x4 box, so that we have
// more than one vector of work per scanline.
Console.WriteLine("Evaluating gradient with x_inner vectorized ");
var output = gradient.Realize <int>(8, 4);
// See figures/lesson_05_vectors.gif for a visualization.
Console.WriteLine("Equivalent C:");
for (int yy = 0; yy < 4; yy++)
{
for (int xOuter = 0; xOuter < 2; xOuter++)
{
// The loop over x_inner has gone away, and has been
// replaced by a vectorized version of the
// expression. On x86 processors, Halide generates SSE
// for all of this.
int[] x_vec = { xOuter * 4 + 0,
xOuter * 4 + 1,
xOuter * 4 + 2,
xOuter * 4 + 3 };
int[] val = { x_vec[0] + yy,
x_vec[1] + yy,
x_vec[2] + yy,
x_vec[3] + yy };
Console.WriteLine($"Evaluating at " +
$"<{x_vec[0]}, {x_vec[1]}, {x_vec[2]}, {x_vec[3]}>, " +
$"<{yy}, {yy}, {yy}, {yy}>: " +
$"<{val[0]}, {val[1]}, {val[2]}, {val[3]}>");
}
}
Console.WriteLine();
Console.WriteLine("Pseudo-code for the schedule:");
gradient.PrintLoopNest();
Console.WriteLine();
}
// Unrolling a loop.
{
var gradient = new HSFunc("gradient_unroll");
gradient[x, y] = x + y;
gradient.TraceStores();
// If multiple pixels share overlapping data, it can make
// sense to unroll a computation so that shared values are
// only computed or loaded once. We do this similarly to how
// we expressed vectorizing. We split a dimension and then
// fully unroll the loop of the inner variable. Unrolling
// doesn't change the order in which things are evaluated.
var x_outer = new HSVar("x_outer");
var x_inner = new HSVar("x_inner");
gradient.Split(x, x_outer, x_inner, 2);
gradient.Unroll(x_inner);
// The shorthand for this is:
// gradient.unroll(x, 2);
Console.WriteLine("Evaluating gradient unrolled by a factor of two");
var result = gradient.Realize <int>(4, 4);
Console.WriteLine("Equivalent C:");
for (int yy = 0; yy < 4; yy++)
{
for (int xOuter = 0; xOuter < 2; xOuter++)
{
// Instead of a for loop over x_inner, we get two
// copies of the innermost statement.
{
int xInner = 0;
int xx = xOuter * 2 + xInner;
Console.WriteLine($"Evaluating at x = {xx}, y = {yy}: {xx + yy}");
}
{
int xInner = 1;
int xx = xOuter * 2 + xInner;
Console.WriteLine($"Evaluating at x = {xx}, y = {yy}: {xx + yy}");
}
}
}
Console.WriteLine();
Console.WriteLine("Pseudo-code for the schedule:");
gradient.PrintLoopNest();
Console.WriteLine();
}
// Splitting by factors that don't divide the extent.
{
var gradient = new HSFunc("gradient_split_7x2");
gradient[x, y] = x + y;
gradient.TraceStores();
// Splitting guarantees that the inner loop runs from zero to
// the split factor, which is important for the uses we saw
// above. So what happens when the total extent we wish to
// evaluate x over isn't a multiple of the split factor? We'll
// split by a factor three, and we'll evaluate gradient over a
// 7x2 box instead of the 4x4 box we've been using.
var x_outer = new HSVar("x_outer");
var x_inner = new HSVar("x_inner");
gradient.Split(x, x_outer, x_inner, 3);
Console.WriteLine("Evaluating gradient over a 7x2 box with x split by three ");
var output = gradient.Realize <int>(7, 2);
// See figures/lesson_05_split_7_by_3.gif for a visualization
// of what happened. Note that some points get evaluated more
// than once!
Console.WriteLine("Equivalent C:");
for (int yy = 0; yy < 2; yy++)
{
for (int xOuter = 0; xOuter < 3; xOuter++) // Now runs from 0 to 2
{
for (int xInner = 0; xInner < 3; xInner++)
{
int xx = xOuter * 3;
// Before we add x_inner, make sure we don't
// evaluate points outside of the 7x2 box. We'll
// clamp x to be at most 4 (7 minus the split
// factor).
if (xx > 4)
{
xx = 4;
}
xx += xInner;
Console.WriteLine($"Evaluating at x = {xx}, y = {yy}: {xx + yy}");
}
}
}
Console.WriteLine();
Console.WriteLine("Pseudo-code for the schedule:");
gradient.PrintLoopNest();
Console.WriteLine();
// If you read the output, you'll see that some coordinates
// were evaluated more than once. That's generally OK, because
// pure Halide functions have no side-effects, so it's safe to
// evaluate the same point multiple times. If you're calling
// out to C functions like we are, it's your responsibility to
// make sure you can handle the same point being evaluated
// multiple times.
// The general rule is: If we require x from x_min to x_min + x_extent, and
// we split by a factor 'factor', then:
//
// x_outer runs from 0 to (x_extent + factor - 1)/factor
// x_inner runs from 0 to factor
// x = min(x_outer * factor, x_extent - factor) + x_inner + x_min
//
// In our example, x_min was 0, x_extent was 7, and factor was 3.
// However, if you write a Halide function with an update
// definition (see lesson 9), then it is not safe to evaluate
// the same point multiple times, so we won't apply this
// trick. Instead the range of values computed will be rounded
// up to the next multiple of the split factor.
}
// Fusing, tiling, and parallelizing.
{
// We saw in the previous lesson that we can parallelize
// across a variable. Here we combine it with fusing and
// tiling to express a useful pattern - processing tiles in
// parallel.
// This is where fusing shines. Fusing helps when you want to
// parallelize across multiple dimensions without introducing
// nested parallelism. Nested parallelism (parallel for loops
// within parallel for loops) is supported by Halide, but
// often gives poor performance compared to fusing the
// parallel variables into a single parallel for loop.
var gradient = new HSFunc("gradient_fused_tiles");
gradient[x, y] = x + y;
gradient.TraceStores();
// First we'll tile, then we'll fuse the tile indices and
// parallelize across the combination.
var x_outer = new HSVar("x_outer");
var y_outer = new HSVar("y_outer");
var x_inner = new HSVar("x_inner");
var y_inner = new HSVar("y_inner");
var tile_index = new HSVar("tile_index");
gradient.Tile(x, y, x_outer, y_outer, x_inner, y_inner, 4, 4);
gradient.Fuse(x_outer, y_outer, tile_index);
gradient.Parallel(tile_index);
// The scheduling calls all return a reference to the Func, so
// you can also chain them together into a single statement to
// make things slightly clearer:
//
// gradient
// .tile(x, y, x_outer, y_outer, x_inner, y_inner, 2, 2)
// .fuse(x_outer, y_outer, tile_index)
// .parallel(tile_index);
Console.WriteLine("Evaluating gradient tiles in parallel");
var output = gradient.Realize <int>(8, 8);
// The tiles should occur in arbitrary order, but within each
// tile the pixels will be traversed in row-major order. See
// figures/lesson_05_parallel_tiles.gif for a visualization.
Console.WriteLine("Equivalent (serial) C:\n");
// This outermost loop should be a parallel for loop, but that's hard in C.
for (int ti = 0; ti < 4; ti++)
{
int yOuter = ti / 2;
int xOuter = ti % 2;
for (int j_inner = 0; j_inner < 4; j_inner++)
{
for (int i_inner = 0; i_inner < 4; i_inner++)
{
int j = yOuter * 4 + j_inner;
int i = xOuter * 4 + i_inner;
Console.WriteLine($"Evaluating at x = {i}, y = {j}: {i + j}");
}
}
}
Console.WriteLine();
Console.WriteLine("Pseudo-code for the schedule:");
gradient.PrintLoopNest();
Console.WriteLine();
}
// Putting it all together.
{
// Are you ready? We're going to use all of the features above now.
var gradient_fast = new HSFunc("gradient_fast");
gradient_fast[x, y] = x + y;
// We'll process 64x64 tiles in parallel.
var x_outer = new HSVar("x_outer");
var y_outer = new HSVar("y_outer");
var x_inner = new HSVar("x_inner");
var y_inner = new HSVar("y_inner");
var tile_index = new HSVar("tile_index");
gradient_fast
.Tile(x, y, x_outer, y_outer, x_inner, y_inner, 64, 64)
.Fuse(x_outer, y_outer, tile_index)
.Parallel(tile_index);
// We'll compute two scanlines at once while we walk across
// each tile. We'll also vectorize in x. The easiest way to
// express this is to recursively tile again within each tile
// into 4x2 subtiles, then vectorize the subtiles across x and
// unroll them across y:
var x_inner_outer = new HSVar("x_inner_outer");
var y_inner_outer = new HSVar("y_inner_outer");
var x_vectors = new HSVar("x_vectors");
var y_pairs = new HSVar("y_pairs");
gradient_fast
.Tile(x_inner, y_inner, x_inner_outer, y_inner_outer, x_vectors, y_pairs, 4, 2)
.Vectorize(x_vectors)
.Unroll(y_pairs);
// Note that we didn't do any explicit splitting or
// reordering. Those are the most important primitive
// operations, but mostly they are buried underneath tiling,
// vectorizing, or unrolling calls.
// Now let's evaluate this over a range which is not a
// multiple of the tile size.
// If you like you can turn on tracing, but it's going to
// produce a lot of printfs. Instead we'll compute the answer
// both in C and Halide and see if the answers match.
var result = gradient_fast.Realize <int>(350, 250);
// See figures/lesson_05_fast.mp4 for a visualization.
Console.WriteLine("Checking Halide result against equivalent C...");
for (int tileIndex = 0; tileIndex < 6 * 4; tileIndex++)
{
int yOuter = tileIndex / 4;
int xOuter = tileIndex % 4;
for (int yInnerOuter = 0; yInnerOuter < 64 / 2; yInnerOuter++)
{
for (int xInnerOuter = 0; xInnerOuter < 64 / 4; xInnerOuter++)
{
// We're vectorized across x
int xx = Math.Min(xOuter * 64, 350 - 64) + xInnerOuter * 4;
int[] xVec = { xx + 0,
xx + 1,
xx + 2,
xx + 3 };
// And we unrolled across y
int yBase = Math.Min(yOuter * 64, 250 - 64) + yInnerOuter * 2;
{
// y_pairs = 0
int yy = yBase + 0;
int[] yVec = { yy, yy, yy, yy };
int[] val = { xVec[0] + yVec[0],
xVec[1] + yVec[1],
xVec[2] + yVec[2],
xVec[3] + yVec[3] };
// Check the result.
for (int i = 0; i < 4; i++)
{
if (result[xVec[i], yVec[i]] != val[i])
{
Console.WriteLine($"There was an error at {xVec[i]} {yVec[i]}!");
return(-1);
}
}
}
{
// y_pairs = 1
int yy = yBase + 1;
int[] yVec = { yy, yy, yy, yy };
int[] val = { xVec[0] + yVec[0],
xVec[1] + yVec[1],
xVec[2] + yVec[2],
xVec[3] + yVec[3] };
// Check the result.
for (int i = 0; i < 4; i++)
{
if (result[xVec[i], yVec[i]] != val[i])
{
Console.WriteLine($"There was an error at {xVec[i]} {yVec[i]}!");
return(-1);
}
}
}
}
}
}
Console.WriteLine();
Console.WriteLine("Pseudo-code for the schedule:");
gradient_fast.PrintLoopNest();
Console.WriteLine();
// Note that in the Halide version, the algorithm is specified
// once at the top, separately from the optimizations, and there
// aren't that many lines of code total. Compare this to the C
// version. There's more code (and it isn't even parallelized or
// vectorized properly). More annoyingly, the statement of the
// algorithm (the result is x plus y) is buried in multiple places
// within the mess. This C code is hard to write, hard to read,
// hard to debug, and hard to optimize further. This is why Halide
// exists.
}
Console.WriteLine("Success!");
return(0);
}