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e0643fa3fc
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/66744 Modified loops in files under fbsource/fbcode/caffe2/ from the format `for(TYPE var=x0;var<x_max;x++)` to the format `for(const auto var: irange(xmax))` This was achieved by running r-barnes's loop upgrader script (D28874212) with some modification to exclude all files under /torch/jit and a number of reversions or unused variable suppression warnings added by hand. Test Plan: Sandcastle Reviewed By: ngimel Differential Revision: D31705358 fbshipit-source-id: d6ea350cbaa8f452fc78f238160e5374be637a48
91 lines
3.0 KiB
C++
91 lines
3.0 KiB
C++
#include <gtest/gtest.h>
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#include <c10/util/irange.h>
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#include <torch/torch.h>
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#include <test/cpp/api/support.h>
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struct OperationTest : torch::test::SeedingFixture {
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protected:
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void SetUp() override {}
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const int TEST_AMOUNT = 10;
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};
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TEST_F(OperationTest, Lerp) {
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for (const auto i : c10::irange(TEST_AMOUNT)) {
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(void)i; // Suppress unused variable warning
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// test lerp_kernel_scalar
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auto start = torch::rand({3, 5});
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auto end = torch::rand({3, 5});
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auto scalar = 0.5;
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// expected and actual
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auto scalar_expected = start + scalar * (end - start);
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auto out = torch::lerp(start, end, scalar);
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// compare
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ASSERT_EQ(out.dtype(), scalar_expected.dtype());
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ASSERT_TRUE(out.allclose(scalar_expected));
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// test lerp_kernel_tensor
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auto weight = torch::rand({3, 5});
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// expected and actual
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auto tensor_expected = start + weight * (end - start);
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out = torch::lerp(start, end, weight);
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// compare
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ASSERT_EQ(out.dtype(), tensor_expected.dtype());
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ASSERT_TRUE(out.allclose(tensor_expected));
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}
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}
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TEST_F(OperationTest, Cross) {
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for (const auto i : c10::irange(TEST_AMOUNT)) {
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(void)i; // Suppress unused variable warning
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// input
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auto a = torch::rand({10, 3});
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auto b = torch::rand({10, 3});
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// expected
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auto exp = torch::empty({10, 3});
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for (const auto j : c10::irange(10)) {
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auto u1 = a[j][0], u2 = a[j][1], u3 = a[j][2];
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auto v1 = b[j][0], v2 = b[j][1], v3 = b[j][2];
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exp[j][0] = u2 * v3 - v2 * u3;
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exp[j][1] = v1 * u3 - u1 * v3;
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exp[j][2] = u1 * v2 - v1 * u2;
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}
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// actual
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auto out = torch::cross(a, b);
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// compare
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ASSERT_EQ(out.dtype(), exp.dtype());
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ASSERT_TRUE(out.allclose(exp));
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}
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}
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TEST_F(OperationTest, Linear_out) {
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{
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const auto x = torch::arange(100., 118).resize_({3, 3, 2});
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const auto w = torch::arange(200., 206).resize_({3, 2});
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const auto b = torch::arange(300., 303);
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auto y = torch::empty({3, 3, 3});
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at::linear_out(y, x, w, b);
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const auto y_exp = torch::tensor(
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{{{40601, 41004, 41407}, {41403, 41814, 42225}, {42205, 42624, 43043}},
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{{43007, 43434, 43861}, {43809, 44244, 44679}, {44611, 45054, 45497}},
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{{45413, 45864, 46315}, {46215, 46674, 47133}, {47017, 47484, 47951}}},
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torch::kFloat);
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ASSERT_TRUE(torch::allclose(y, y_exp));
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}
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{
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const auto x = torch::arange(100., 118).resize_({3, 3, 2});
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const auto w = torch::arange(200., 206).resize_({3, 2});
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auto y = torch::empty({3, 3, 3});
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at::linear_out(y, x, w);
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ASSERT_EQ(y.ndimension(), 3);
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ASSERT_EQ(y.sizes(), torch::IntArrayRef({3, 3, 3}));
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const auto y_exp = torch::tensor(
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{{{40301, 40703, 41105}, {41103, 41513, 41923}, {41905, 42323, 42741}},
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{{42707, 43133, 43559}, {43509, 43943, 44377}, {44311, 44753, 45195}},
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{{45113, 45563, 46013}, {45915, 46373, 46831}, {46717, 47183, 47649}}},
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torch::kFloat);
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ASSERT_TRUE(torch::allclose(y, y_exp));
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}
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}
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