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#include "Halide.h"
#include <stdio.h>
using namespace Halide;
bool vector_store;
bool scalar_store;
uint16_t vector_store_lanes;
void reset_trace() {
vector_store_lanes = 0;
vector_store = scalar_store = false;
}
// A trace that checks for vector and scalar stores
int my_trace(JITUserContext *user_context, const halide_trace_event_t *ev) {
if (ev->event == halide_trace_store) {
if (ev->type.lanes > 1) {
vector_store = true;
vector_store_lanes = ev->type.lanes;
} else {
scalar_store = true;
}
}
return 0;
}
// A custom allocator that counts how many allocations are for empty buffers.
int empty_allocs = 0, nonempty_allocs = 0, frees = 0;
void reset_alloc_counts() {
empty_allocs = nonempty_allocs = frees = 0;
}
void *my_malloc(JITUserContext *ctx, size_t sz) {
// Don't worry about alignment because we'll just test this with scalar code
if (sz == 0) {
empty_allocs++;
} else {
nonempty_allocs++;
}
return malloc(sz);
}
void my_free(JITUserContext *ctx, void *ptr) {
frees++;
free(ptr);
}
// Custom lowering pass to count the number of IfThenElse statements found inside
// ProducerConsumer nodes.
int if_then_else_count = 0;
class CountIfThenElse : public Internal::IRMutator {
int producer_consumers;
public:
CountIfThenElse()
: producer_consumers(0) {
}
Internal::Stmt visit(const Internal::ProducerConsumer *op) override {
// Only count ifs found inside a pipeline.
producer_consumers++;
Internal::Stmt stmt = IRMutator::visit(op);
producer_consumers--;
return stmt;
}
Internal::Stmt visit(const Internal::IfThenElse *op) override {
if (producer_consumers > 0) {
if_then_else_count++;
}
return Internal::IRMutator::visit(op);
}
using Internal::IRMutator::visit;
};
int main(int argc, char **argv) {
if (get_jit_target_from_environment().arch == Target::WebAssembly) {
printf("[SKIP] WebAssembly JIT does not support custom allocators.\n");
return 0;
}
{
Param<bool> param;
Func f;
Var x;
f(x) = select(param, x * 3, x * 17);
// Vectorize when the output is large enough
Expr cond = (f.output_buffer().width() >= 4);
f.specialize(cond).vectorize(x, 4);
// This has created a specialization of f that is
// vectorized. Now we want to further specialize both the
// default case and the special case based on param. We can
// retrieve a reference to the specialization using the same
// condition again:
f.specialize(cond).specialize(param);
// Now specialize the narrow case on param as well
f.specialize(param);
f.jit_handlers().custom_trace = my_trace;
f.trace_stores();
Buffer<int> out(100);
// Just check that all the specialization didn't change the output.
param.set(true);
reset_trace();
f.realize(out);
for (int i = 0; i < out.width(); i++) {
int correct = i * 3;
if (out(i) != correct) {
printf("out(%d) was %d instead of %d\n",
i, out(i), correct);
}
}
param.set(false);
f.realize(out);
for (int i = 0; i < out.width(); i++) {
int correct = i * 17;
if (out(i) != correct) {
printf("out(%d) was %d instead of %d\n",
i, out(i), correct);
}
}
// Should have used vector stores
if (!vector_store || scalar_store) {
printf("This was supposed to use vector stores\n");
return 1;
}
// Now try a smaller input
out = Buffer<int>(3);
param.set(true);
reset_trace();
f.realize(out);
for (int i = 0; i < out.width(); i++) {
int correct = i * 3;
if (out(i) != correct) {
printf("out(%d) was %d instead of %d\n",
i, out(i), correct);
}
}
param.set(false);
f.realize(out);
for (int i = 0; i < out.width(); i++) {
int correct = i * 17;
if (out(i) != correct) {
printf("out(%d) was %d instead of %d\n",
i, out(i), correct);
}
}
// Should have used scalar stores
if (vector_store || !scalar_store) {
printf("This was supposed to use scalar stores\n");
return 1;
}
}
{
Func f1, f2, g1, g2;
Var x;
// Define pipeline A
f1(x) = x + 7;
g1(x) = f1(x) + f1(x + 1);
// Define pipeline B
f2(x) = x * 34;
g2(x) = f2(x) + f2(x - 1);
// Switch between them based on a boolean param
Param<bool> param;
Func out;
out(x) = select(param, g1(x), g2(x));
// These will be outside the condition that specializes out,
// but skip stages will nuke their allocation and computation
// for us.
f1.compute_root();
g1.compute_root();
f2.compute_root();
out.specialize(param);
// Count allocations.
out.jit_handlers().custom_malloc = my_malloc;
out.jit_handlers().custom_free = my_free;
reset_alloc_counts();
param.set(true);
out.realize({100});
if (empty_allocs != 1 || nonempty_allocs != 2 || frees != 3) {
printf("There were supposed to be 1 empty alloc, 2 nonempty allocs, and 3 frees.\n"
"Instead we got %d empty allocs, %d nonempty allocs, and %d frees.\n",
empty_allocs, nonempty_allocs, frees);
return 1;
}
reset_alloc_counts();
param.set(false);
out.realize({100});
if (empty_allocs != 2 || nonempty_allocs != 1 || frees != 3) {
printf("There were supposed to be 2 empty allocs, 1 nonempty alloc, and 3 frees.\n"
"Instead we got %d empty allocs, %d nonempty allocs, and %d frees.\n",
empty_allocs, nonempty_allocs, frees);
return 1;
}
}
{
// Specialize for interleaved vs planar inputs
ImageParam im(Int(32), 1);
im.dim(0).set_stride(Expr()); // unconstrain the stride
Func f;
Var x;
f(x) = im(x);
// If we have a stride of 1 it's worth vectorizing, but only if the width is also > 8.
f.specialize(im.dim(0).stride() == 1 && im.width() >= 8).vectorize(x, 8);
f.trace_stores();
f.jit_handlers().custom_trace = &my_trace;
// Check bounds inference is still cool with widths < 8
f.infer_input_bounds({5});
int m = im.get().min(0), e = im.get().extent(0);
if (m != 0 || e != 5) {
printf("min, extent = %d, %d instead of 0, 5\n", m, e);
return 1;
}
// Check we don't crash with the small input, and that it uses scalar stores
reset_trace();
f.realize({5});
if (!scalar_store || vector_store) {
printf("These stores were supposed to be scalar.\n");
return 1;
}
// Check we don't crash with a larger input, and that it uses vector stores
Buffer<int> image(100);
im.set(image);
reset_trace();
f.realize({100});
if (scalar_store || !vector_store) {
printf("These stores were supposed to be vector.\n");
return 1;
}
}
{
// Specialize a copy for dense vs. non-dense inputs.
ImageParam im(Int(32), 1);
im.dim(0).set_stride(Expr()); // unconstrain the stride
Func f;
Var x;
f(x) = im(x);
f.specialize(im.dim(0).stride() == 1).vectorize(x, 8);
f.trace_stores();
f.jit_handlers().custom_trace = &my_trace;
Buffer<int> strided_image(4, 100);
strided_image.slice(0, 0);
im.set(strided_image);
// Check we used scalar stores for a strided input.
reset_trace();
f.realize({100});
if (!scalar_store || vector_store) {
printf("These stores were supposed to be scalar.\n");
return 1;
}
// Check that we used vector stores for a dense input.
Buffer<int> image(100);
im.set(image);
reset_trace();
f.realize({100});
if (scalar_store || !vector_store) {
printf("These stores were supposed to be vector.\n");
return 1;
}
}
{
// Bounds required of the input change depending on the param
ImageParam im(Int(32), 1);
Param<bool> param;
Func f;
Var x;
f(x) = select(param, im(x + 10), im(x - 10));
f.specialize(param);
param.set(true);
f.infer_input_bounds({100});
int m = im.get().min(0);
if (m != 10) {
printf("min %d instead of 10\n", m);
return 1;
}
param.set(false);
im.reset();
f.infer_input_bounds({100});
m = im.get().min(0);
if (m != -10) {
printf("min %d instead of -10\n", m);
return 1;
}
}
{
// Specialize an update definition
Func f;
Var x;
Param<int> start, size;
RDom r(start, size);
f(x) = x;
f(r) = 10 - r;
// Special-case for when we only update one element of f
f.update().specialize(size == 1);
// Also special-case updating no elements of f
f.update().specialize(size == 0);
start.set(0);
size.set(1);
// Not crashing is enough
f.realize({100});
}
{
// What happens to bounds inference if an input is not used at
// all for a given specialization?
ImageParam im(Int(32), 1);
Param<bool> param;
Func f;
Var x;
f(x) = select(param, im(x), 0);
f.specialize(param);
param.set(false);
Buffer<int> image(10);
im.set(image);
// The image is too small, but that should be OK, because the
// param is false so the image will never be used.
f.realize({100});
}
{
// Specialization inherits the scheduling directives done so far:
ImageParam im(Int(32), 2);
Func f;
Var x, y;
f(x, y) = im(x, y);
Expr cond = f.output_buffer().width() >= 4;
// Unroll y by two innermost.
f.reorder(y, x).unroll(y, 2).reorder(x, y);
// Vectorize if the output is at least 4-wide. Inherits the
// unrolling already done.
f.specialize(cond).vectorize(x, 4);
// Confirm that the unrolling applies to both cases using bounds inference:
f.infer_input_bounds({3, 1});
if (im.get().extent(0) != 3) {
printf("extent(0) was supposed to be 3.\n");
return 1;
}
if (im.get().extent(1) != 2) {
// Height is 2, because the unrolling also happens in the
// specialized case.
printf("extent(1) was supposed to be 2.\n");
return 1;
}
}
{
// Check we don't need to specialize intermediate stages.
ImageParam im(Int(32), 1);
Func f, g, h, out;
Var x;
f(x) = im(x);
g(x) = f(x);
h(x) = g(x);
out(x) = h(x);
Expr w = out.output_buffer().dim(0).extent();
out.output_buffer().dim(0).set_min(0);
f.compute_root().specialize(w >= 4).vectorize(x, 4);
g.compute_root().vectorize(x, 4);
h.compute_root().vectorize(x, 4);
out.specialize(w >= 4).vectorize(x, 4);
Buffer<int> input(3), output(3);
// Shouldn't throw a bounds error:
im.set(input);
out.realize(output);
}
{
// Check specializations of stages nested in other stages simplify appropriately.
ImageParam im(Int(32), 2);
Param<bool> cond1, cond2;
Func f, out;
Var x, y;
f(x, y) = im(x, y);
out(x, y) = f(x, y);
f.compute_at(out, x).specialize(cond1 && cond2).vectorize(x, 4, TailStrategy::RoundUp);
out.compute_root().specialize(cond1 && cond2).vectorize(x, 4, TailStrategy::RoundUp);
if_then_else_count = 0;
CountIfThenElse pass1;
for (auto ff : out.compile_to_module(out.infer_arguments()).functions()) {
pass1.mutate(ff.body);
}
Buffer<int> input(3, 3), output(3, 3);
// Shouldn't throw a bounds error:
im.set(input);
cond1.set(false);
cond2.set(false);
out.realize(output);
if (if_then_else_count != 1) {
printf("Expected 1 IfThenElse stmts. Found %d.\n", if_then_else_count);
return 1;
}
}
{
// Check specializations of stages nested in other stages simplify appropriately.
ImageParam im(Int(32), 2);
Param<bool> cond1, cond2;
Func f, out;
Var x, y;
f(x, y) = im(x, y);
out(x, y) = f(x, y);
f.compute_at(out, x).specialize(cond1).vectorize(x, 4, TailStrategy::RoundUp);
out.compute_root().specialize(cond1 && cond2).vectorize(x, 4, TailStrategy::RoundUp);
if_then_else_count = 0;
CountIfThenElse pass2;
for (auto ff : out.compile_to_module(out.infer_arguments()).functions()) {
pass2.mutate(ff.body);
}
Buffer<int> input(3, 3), output(3, 3);
// Shouldn't throw a bounds error:
im.set(input);
cond1.set(false);
cond2.set(false);
out.realize(output);
// There should have been 2 Ifs total: They are the
// outer cond1 && cond2, and the condition in the true case
// should have been simplified away. The If in the false
// branch cannot be simplified.
if (if_then_else_count != 2) {
printf("Expected 2 IfThenElse stmts. Found %d.\n", if_then_else_count);
return 1;
}
}
{
// Check specialization on a more complex expression used in a select.
ImageParam im(Int(32), 2);
Param<int> p;
Expr test = (p > 73) || (p * p + p + 1 == 0);
Func f;
Var x;
f(x) = select(test, im(x, 0), im(0, x));
f.specialize(test);
// Selects evaluate both sides, so evaluating ten values of
// this Func (ignoring the specialization) requires a 10x10
// box of the input (The union of a 10x1 box and a 1x10
// box). The specialization means that instead of depending on
// the union, we either depend on a wide or a tall box,
// depending on the param.
p.set(100);
f.infer_input_bounds({10});
int w = im.get().width();
int h = im.get().height();
if (w != 10 || h != 1) {
printf("Incorrect inferred size: %d %d\n", w, h);
return 1;
}
im.reset();
p.set(-100);
f.infer_input_bounds({10});
w = im.get().width();
h = im.get().height();
if (w != 1 || h != 10) {
printf("Incorrect inferred size: %d %d\n", w, h);
return 1;
}
}
{
// Check specialization of an implied condition
ImageParam im(Int(32), 2);
Param<int> p;
Expr test = (p > 73);
Func f;
Var x;
f(x) = select(p > 50, im(x, 0), im(0, x));
f.specialize(test);
// (p > 73) implies (p > 50), so if the condition holds (as it
// does when p is 100), we only access the first row of the
// input, and bounds inference should recognize this.
p.set(100);
f.infer_input_bounds({10});
int w = im.get().width();
int h = im.get().height();
if (w != 10 || h != 1) {
printf("Incorrect inferred size: %d %d\n", w, h);
return 1;
}
im.reset();
// (p <= 73) doesn't tell us anything about (p > 50), so when
// the condition doesn't hold, we can make no useful
// simplifications. The select remains, so both sides of it
// are evaluated, so the image must be loaded over the full
// square.
p.set(-100);
f.infer_input_bounds({10});
w = im.get().width();
h = im.get().height();
if (w != 10 || h != 10) {
printf("Incorrect inferred size: %d %d\n", w, h);
return 1;
}
}
{
Var x, y;
Param<int> p;
Expr const_false = Expr(0) == Expr(1);
Expr const_true = Expr(0) == Expr(0);
Expr different_const_true = Expr(1) == Expr(1);
// Check that we aggressively prune specialize(const-false)
Func f;
f(x) = x;
f.specialize(p == 0).vectorize(x, 32); // will *not* be pruned
f.specialize(const_false).vectorize(x, 8); // will be pruned
f.vectorize(x, 4); // default case, not a specialization
_halide_user_assert(f.function().definition().specializations().size() == 2);
std::map<std::string, Internal::Function> env;
env.insert({f.function().name(), f.function()});
simplify_specializations(env);
const auto &s = f.function().definition().specializations();
_halide_user_assert(s.size() == 1);
// should be (something) == 0
_halide_user_assert(s[0].condition.as<Internal::EQ>() && is_const_zero(s[0].condition.as<Internal::EQ>()->b));
f.jit_handlers().custom_trace = &my_trace;
f.trace_stores();
vector_store_lanes = 0;
p.set(0);
f.realize({100});
_halide_user_assert(vector_store_lanes == 32);
vector_store_lanes = 0;
p.set(42); // just a nonzero value
f.realize({100});
_halide_user_assert(vector_store_lanes == 4);
}
{
Var x;
Param<int> p;
Expr const_false = Expr(0) == Expr(1);
Expr const_true = Expr(0) == Expr(0);
Expr different_const_true = Expr(1) == Expr(1);
// Check that we aggressively prune all specializations after specialize(const-true)
Func f;
f(x) = x;
f.specialize(p == 0).vectorize(x, 32); // will *not* be pruned
f.specialize(const_true).vectorize(x, 16); // will *not* be pruned
f.specialize(const_false).vectorize(x, 4); // will be pruned
f.specialize(p == 42).vectorize(x, 8); // will be pruned
f.specialize(const_true); // dupe of call above, won't add new specialization
// Note that specialize() will return the same schedule for subsequent
// calls with the same Expr, but doesn't guarantee that all Exprs
// that evaluate to the same value collapse. Use a deliberately-
// different Expr here to check that we do elide these.
f.specialize(different_const_true); // will be pruned
_halide_user_assert(f.function().definition().specializations().size() == 5);
std::map<std::string, Internal::Function> env;
env.insert({f.function().name(), f.function()});
simplify_specializations(env);
const auto &s = f.function().definition().specializations();
// Note that this is 1 (rather than 2) because the final const-true
// Specialization will be hoisted into the main Schedule.
_halide_user_assert(s.size() == 1);
// should be (something) == 0
_halide_user_assert(s[0].condition.as<Internal::EQ>() && is_const_zero(s[0].condition.as<Internal::EQ>()->b));
f.jit_handlers().custom_trace = &my_trace;
f.trace_stores();
vector_store_lanes = 0;
p.set(42); // Chosen to ensure pruned branch is pruned
f.realize({100});
_halide_user_assert(vector_store_lanes == 16);
vector_store_lanes = 0;
p.set(0);
f.realize({100});
_halide_user_assert(vector_store_lanes == 32);
}
{
Var x;
Param<int> p;
Expr const_true = Expr(0) == Expr(0);
Expr different_const_true = Expr(1) == Expr(1);
// Check that if we promote a final const-true specialize, we keep the
// implicit compute/store_root required for outputs.
Func f("foof");
f(x) = x;
f.specialize(p == 0).vectorize(x, 32); // will *not* be pruned
f.specialize(const_true).vectorize(x, 16);
f.jit_handlers().custom_trace = &my_trace;
f.trace_stores();
vector_store_lanes = 0;
p.set(42); // arbitrary nonzero value
f.realize({100});
_halide_user_assert(vector_store_lanes == 16);
vector_store_lanes = 0;
p.set(0);
f.realize({100});
_halide_user_assert(vector_store_lanes == 32);
}
{
Var x;
Param<int> p;
// Check that specialize_fail() is correctly skipped.
Func f;
f(x) = x;
f.specialize(p == 0);
f.specialize_fail("Unhandled Param value encountered.");
// It's OK to retrieve an existing specialization after specialize_fail()...
f.specialize(p == 0).vectorize(x, 32);
// ...but it's *not* ok to create a new specialization after specialize_fail()
// f.specialize(p == 1); -- would fail
// Also not ok to have duplicate specialize_fail() calls.
// f.specialize_fail("This is bad."); -- would fail
f.jit_handlers().custom_trace = &my_trace;
f.trace_stores();
vector_store_lanes = 0;
p.set(0);
f.realize({100});
_halide_user_assert(vector_store_lanes == 32);
}
printf("Success!\n");
return 0;
}
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