https://github.com/halide/Halide
Tip revision: b7a20b6962324d217461c44436524afaa2a4fe4d authored by Derek Gerstmann on 08 May 2023, 21:52:41 UTC
Fix missing initializer for vulkan memory config that got munged in a previous merge.
Fix missing initializer for vulkan memory config that got munged in a previous merge.
Tip revision: b7a20b6
test_function_dag.cpp
#include "FunctionDAG.h"
#include "Halide.h"
#include <sstream>
using namespace Halide;
extern "C" int mul_by_two(
halide_buffer_t *input,
halide_buffer_t *output) {
if (input->is_bounds_query()) {
// Bounds query: infer the input dimensions from the output dimensions. In
// this example, the dimensions are exactly the same
for (int i = 0; i < 2; ++i) {
input->dim[i] = output->dim[i];
}
return 0;
}
// Actual computation: return 2 times x as an example. The first dimension is
// the innermost, so iterate over it last to avoid inefficient memory access
// patterns.
for (int j = 0; j < input->dim[1].extent; ++j) {
for (int i = 0; i < input->dim[0].extent; ++i) {
float *out = (float *)output->host + i * output->dim[0].stride +
j * output->dim[1].stride;
float *in = (float *)input->host + i * input->dim[0].stride +
j * input->dim[1].stride;
(*out) = 2 * (*in);
}
}
return 0;
}
void test_coeff_wise(const Target &target) {
Var x("x"), y("y");
std::ostringstream with_extern;
{
Func f("f"), g("g"), h("h");
f(x, y) = (x + y) * (x + y);
Halide::ExternFuncArgument arg = f;
std::vector<Var> vars = {x, y};
Halide::Type input_type = Halide::Float(32);
g.define_extern(
"mul_by_two",
{arg},
input_type,
vars,
Halide::NameMangling::C);
g.function().extern_definition_proxy_expr() = f(x, y) * 2.0f;
h(x, y) = g(x, y) * 2 + 1;
h.set_estimate(x, 0, 1000).set_estimate(y, 0, 1000);
std::vector<Halide::Internal::Function> v;
v.push_back(h.function());
Halide::Internal::Autoscheduler::FunctionDAG d(v, target);
d.dump(with_extern);
}
std::ostringstream without_extern;
{
Func f("f"), g("g"), h("h");
f(x, y) = (x + y) * (x + y);
g(x, y) = f(x, y) * 2.0f;
h(x, y) = g(x, y) * 2 + 1;
h.set_estimate(x, 0, 1000).set_estimate(y, 0, 1000);
std::vector<Halide::Internal::Function> v;
v.push_back(h.function());
Halide::Internal::Autoscheduler::FunctionDAG d(v, target);
d.dump(without_extern);
}
// Disabled for now: there is still work to do to populate the jacobian
// assert(with_extern.str() == without_extern.str());
}
extern "C" int matmul(
halide_buffer_t *input1,
halide_buffer_t *input2,
halide_buffer_t *output) {
if (input1->is_bounds_query() || input2->is_bounds_query()) {
// Bounds query: infer the input dimensions from the output dimensions.
// We leave the k dimension alone since we can't infer it from the output dimensions.
input1->dim[0].min = output->dim[0].min;
input1->dim[0].extent = output->dim[0].extent;
input2->dim[1].min = output->dim[1].min;
input2->dim[1].extent = output->dim[1].extent;
return 0;
}
// Actual computation: return input1 * input2.
const int max_i = output->dim[0].min + output->dim[0].extent;
const int max_j = output->dim[1].min + output->dim[1].extent;
for (int i = output->dim[0].min; i < max_i; ++i) {
for (int j = output->dim[1].min; j < max_j; ++j) {
int pos[2] = {i, j};
float *out = (float *)output->address_of(pos);
*out = 0.0f;
for (int k = 0; k < input1->dim[1].extent; ++k) {
int pos1[2] = {i, k};
float *in1 = (float *)input1->address_of(pos1);
int pos2[2] = {k, j};
float *in2 = (float *)input2->address_of(pos2);
(*out) += (*in1) * (*in2);
}
}
}
return 0;
}
void test_matmul(const Target &target) {
Var x("x"), y("y"), k("k");
RDom r(0, 200);
Halide::Buffer<float> input1(200, 200);
Halide::Buffer<float> input2(200, 200);
std::ostringstream with_extern;
{
Func mm("mm"), h("h");
Halide::ExternFuncArgument arg1 = input1;
Halide::ExternFuncArgument arg2 = input2;
std::vector<Var> vars = {x, y};
Halide::Type input_type = Halide::Float(32);
mm.define_extern(
"matmul",
{arg1, arg2},
{input_type, input_type},
vars,
Halide::NameMangling::C);
mm.function().extern_definition_proxy_expr() = Halide::sum(input1(x, r) * input2(r, y));
h(x, y) = mm(x, y);
h.set_estimate(x, 0, 200).set_estimate(y, 0, 200);
std::vector<Halide::Internal::Function> v;
v.push_back(h.function());
Halide::Internal::Autoscheduler::FunctionDAG d(v, target);
d.dump(with_extern);
}
std::ostringstream without_extern;
{
Func mm("mm"), h("h");
mm(x, y) = Halide::sum(input1(x, r) * input2(r, y));
h(x, y) = mm(x, y);
h.set_estimate(x, 0, 200).set_estimate(y, 0, 200);
std::vector<Halide::Internal::Function> v;
v.push_back(h.function());
Halide::Internal::Autoscheduler::FunctionDAG d(v, target);
d.dump(without_extern);
}
std::cout << "with_extern:\n " << with_extern.str()
<< "\n\nwithout_extern:\n " << without_extern.str() << "\n";
}
int main(int argc, char **argv) {
// Use a fixed target for the analysis to get consistent results from this test.
Target target("x86-64-linux-sse41-avx-avx2");
test_coeff_wise(target);
test_matmul(target);
return 0;
}