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#include <gtest/gtest.h>
#include <c10/util/irange.h>
#include <torch/torch.h>
#include <test/cpp/api/support.h>
struct OperationTest : torch::test::SeedingFixture {
protected:
void SetUp() override {}
const int TEST_AMOUNT = 10;
};
TEST_F(OperationTest, Lerp) {
for (const auto i : c10::irange(TEST_AMOUNT)) {
(void)i; // Suppress unused variable warning
// test lerp_kernel_scalar
auto start = torch::rand({3, 5});
auto end = torch::rand({3, 5});
auto scalar = 0.5;
// expected and actual
auto scalar_expected = start + scalar * (end - start);
auto out = torch::lerp(start, end, scalar);
// compare
ASSERT_EQ(out.dtype(), scalar_expected.dtype());
ASSERT_TRUE(out.allclose(scalar_expected));
// test lerp_kernel_tensor
auto weight = torch::rand({3, 5});
// expected and actual
auto tensor_expected = start + weight * (end - start);
out = torch::lerp(start, end, weight);
// compare
ASSERT_EQ(out.dtype(), tensor_expected.dtype());
ASSERT_TRUE(out.allclose(tensor_expected));
}
}
TEST_F(OperationTest, Cross) {
for (const auto i : c10::irange(TEST_AMOUNT)) {
(void)i; // Suppress unused variable warning
// input
auto a = torch::rand({10, 3});
auto b = torch::rand({10, 3});
// expected
auto exp = torch::empty({10, 3});
for (const auto j : c10::irange(10)) {
auto u1 = a[j][0], u2 = a[j][1], u3 = a[j][2];
auto v1 = b[j][0], v2 = b[j][1], v3 = b[j][2];
exp[j][0] = u2 * v3 - v2 * u3;
exp[j][1] = v1 * u3 - u1 * v3;
exp[j][2] = u1 * v2 - v1 * u2;
}
// actual
auto out = torch::cross(a, b);
// compare
ASSERT_EQ(out.dtype(), exp.dtype());
ASSERT_TRUE(out.allclose(exp));
}
}
TEST_F(OperationTest, Linear_out) {
{
const auto x = torch::arange(100., 118).resize_({3, 3, 2});
const auto w = torch::arange(200., 206).resize_({3, 2});
const auto b = torch::arange(300., 303);
auto y = torch::empty({3, 3, 3});
at::linear_out(y, x, w, b);
const auto y_exp = torch::tensor(
{{{40601, 41004, 41407}, {41403, 41814, 42225}, {42205, 42624, 43043}},
{{43007, 43434, 43861}, {43809, 44244, 44679}, {44611, 45054, 45497}},
{{45413, 45864, 46315}, {46215, 46674, 47133}, {47017, 47484, 47951}}},
torch::kFloat);
ASSERT_TRUE(torch::allclose(y, y_exp));
}
{
const auto x = torch::arange(100., 118).resize_({3, 3, 2});
const auto w = torch::arange(200., 206).resize_({3, 2});
auto y = torch::empty({3, 3, 3});
at::linear_out(y, x, w);
ASSERT_EQ(y.ndimension(), 3);
ASSERT_EQ(y.sizes(), torch::IntArrayRef({3, 3, 3}));
const auto y_exp = torch::tensor(
{{{40301, 40703, 41105}, {41103, 41513, 41923}, {41905, 42323, 42741}},
{{42707, 43133, 43559}, {43509, 43943, 44377}, {44311, 44753, 45195}},
{{45113, 45563, 46013}, {45915, 46373, 46831}, {46717, 47183, 47649}}},
torch::kFloat);
ASSERT_TRUE(torch::allclose(y, y_exp));
}
}
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