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[caffe2] fix build failures in optimized builds under clang

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Summary:
There are various possible approaches, but the approach chosen minimizes disruption to source control blame.

Addresses:
```
error: Function _ZN23FunctionalTest_Pad_Test8TestBodyEv is too big to optimize [-Werror,-Wignored-optimization-argument]
```

Test Plan: buck2 build mode/opt caffe2/test/cpp/api:functional

Reviewed By: jamesr66a

Differential Revision: D34027291

fbshipit-source-id: 9dfd771ad56d3d4bc0d41b38b04654c8dae7c006
(cherry picked from commit d43b5a7ed6)
This commit is contained in:
Yedidya Feldblum 2022-02-22 13:39:48 -08:00 committed by PyTorch MergeBot
parent 600f4bf20c
commit 7a5b0efc64
1 changed files with 15 additions and 1 deletions
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16
test/cpp/api/functional.cpp
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@ -2174,7 +2174,7 @@ TEST_F(FunctionalTest, Interpolate) {
}
}
TEST_F(FunctionalTest, Pad) {
TEST_F(FunctionalTest, Pad1) {
{
auto input = torch::arange(6, torch::kDouble).reshape({1, 2, 3});
auto output = F::pad(input, F::PadFuncOptions({1, 2}).mode(torch::kCircular));
@ -2183,6 +2183,8 @@ TEST_F(FunctionalTest, Pad) {
ASSERT_EQ(output.sizes(), std::vector<int64_t>({1, 2, 6}));
ASSERT_TRUE(output.allclose(expected, 1e-04));
}
}
TEST_F(FunctionalTest, Pad2) {
{
auto input = torch::arange(9, torch::kDouble).reshape({1, 1, 3, 3});
auto output = F::pad(input, F::PadFuncOptions({3, 3, 3, 1}).mode(torch::kCircular));
@ -2197,6 +2199,8 @@ TEST_F(FunctionalTest, Pad) {
ASSERT_EQ(output.sizes(), std::vector<int64_t>({1, 1, 7, 9}));
ASSERT_TRUE(output.allclose(expected, 1e-04));
}
}
TEST_F(FunctionalTest, Pad3) {
{
auto input = torch::arange(12, torch::kDouble).reshape({1, 1, 2, 2, 3});
auto output = F::pad(input, F::PadFuncOptions({3, 3, 2, 1, 2, 2}).mode(torch::kCircular));
@ -2239,6 +2243,8 @@ TEST_F(FunctionalTest, Pad) {
ASSERT_EQ(output.sizes(), std::vector<int64_t>({1, 1, 6, 5, 9}));
ASSERT_TRUE(output.allclose(expected, 1e-04));
}
}
TEST_F(FunctionalTest, Pad4) {
{
auto input = torch::arange(16, torch::kDouble).reshape({2, 2, 2, 2});
auto output = F::pad(input, F::PadFuncOptions({1, 1, 1, 1}).mode(torch::kReflect));
@ -2265,6 +2271,8 @@ TEST_F(FunctionalTest, Pad) {
ASSERT_EQ(output.sizes(), std::vector<int64_t>({2, 2, 4, 4}));
ASSERT_TRUE(output.allclose(expected, 1e-04));
}
}
TEST_F(FunctionalTest, Pad5) {
{
auto input = torch::arange(12, torch::kDouble).reshape({1, 1, 2, 2, 3});
auto output = F::pad(input, F::PadFuncOptions({1, 2, 2, 1, 1, 2}).mode(torch::kReplicate));
@ -2301,6 +2309,8 @@ TEST_F(FunctionalTest, Pad) {
ASSERT_EQ(output.sizes(), std::vector<int64_t>({1, 1, 5, 5, 6}));
ASSERT_TRUE(output.allclose(expected, 1e-04));
}
}
TEST_F(FunctionalTest, Pad6) {
{
auto input = torch::arange(18, torch::kDouble).reshape({1, 1, 3, 2, 3});
auto output = F::pad(input, F::PadFuncOptions({0, 2, 1, 0, 1, 2}).mode(torch::kReflect));
@ -2331,12 +2341,16 @@ TEST_F(FunctionalTest, Pad) {
ASSERT_EQ(output.sizes(), std::vector<int64_t>({1, 1, 6, 3, 5}));
ASSERT_TRUE(output.allclose(expected, 1e-04));
}
}
TEST_F(FunctionalTest, Pad7) {
{
auto input = torch::ones({1, 1, 1, 1}, torch::kDouble);
auto output = F::pad(input, F::PadFuncOptions({1, 1}).mode(torch::kConstant).value(0));
ASSERT_EQ(output.sizes(), std::vector<int64_t>({1, 1, 1, 3}));
auto expected = torch::tensor({{{{0., 1., 0.}}}}, torch::kDouble);
}
}
TEST_F(FunctionalTest, Pad8) {
{
auto input = torch::ones({1, 1, 1, 1}, torch::kDouble);
auto output = F::pad(input, F::PadFuncOptions({1, 1}));