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[CI] Update clang-format (#116002)
To 17.0.6 build using https://github.com/pytorch/test-infra/blob/main/.github/workflows/clang-tidy-linux.yml Pull Request resolved: https://github.com/pytorch/pytorch/pull/116002 Approved by: https://github.com/suo
This commit is contained in:
Nikita Shulga
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PyTorch MergeBot
PyTorch MergeBot
parent
c285ca7916
commit
d7caef7996
@ -3271,138 +3271,141 @@ TEST_F(ModulesTest, MarginRankingLoss) {
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}
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TEST_F(ModulesTest, BCEWithLogitsLoss) {
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{// test BCE with logits raises if target and input are different size
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{const auto target = torch::rand(5);
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const auto input = torch::rand({5, 1});
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ASSERT_THROWS_WITH(
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BCEWithLogitsLoss()(input, target), "must be the same as input size");
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}
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{
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const auto target = torch::rand({5, 1});
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const auto input = torch::rand(5);
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ASSERT_THROWS_WITH(
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BCEWithLogitsLoss()(input, target), "must be the same as input size");
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}
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}
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{ // test BCE with logits gives same result as sigmoid and bce loss
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auto sigmoid = Sigmoid();
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{ // test BCE with logits raises if target and input are different size
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{
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const auto target = torch::rand(5);
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const auto input = torch::rand({5, 1});
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ASSERT_THROWS_WITH(
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BCEWithLogitsLoss()(input, target), "must be the same as input size");
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}
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{
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const auto target = torch::rand({5, 1});
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const auto input = torch::rand(5);
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ASSERT_THROWS_WITH(
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BCEWithLogitsLoss()(input, target), "must be the same as input size");
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}
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}
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{ // test BCE with logits gives same result as sigmoid and bce loss
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auto sigmoid = Sigmoid();
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auto target = torch::rand({64, 4});
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auto output = torch::rand({64, 4}) - 0.5;
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auto target = torch::rand({64, 4});
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auto output = torch::rand({64, 4}) - 0.5;
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ASSERT_TRUE(torch::allclose(
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BCEWithLogitsLoss()(output, target), BCELoss()(sigmoid(output), target)));
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ASSERT_TRUE(torch::allclose(
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BCEWithLogitsLoss()(output, target),
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BCELoss()(sigmoid(output), target)));
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auto weight = torch::rand(4);
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ASSERT_TRUE(torch::allclose(
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BCEWithLogitsLoss(BCEWithLogitsLossOptions().weight(weight))(
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output, target),
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BCELoss(BCELossOptions().weight(weight))(sigmoid(output), target)));
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auto weight = torch::rand(4);
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ASSERT_TRUE(torch::allclose(
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BCEWithLogitsLoss(BCEWithLogitsLossOptions().weight(weight))(
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output, target),
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BCELoss(BCELossOptions().weight(weight))(sigmoid(output), target)));
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target = torch::zeros({4, 1}, torch::kFloat);
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output = torch::empty({4, 1}, torch::kFloat).fill_(-100);
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target = torch::zeros({4, 1}, torch::kFloat);
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output = torch::empty({4, 1}, torch::kFloat).fill_(-100);
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ASSERT_TRUE(torch::allclose(
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BCEWithLogitsLoss()(output, target), BCELoss()(sigmoid(output), target)));
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ASSERT_TRUE(torch::allclose(
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BCEWithLogitsLoss()(output, target),
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BCELoss()(sigmoid(output), target)));
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ASSERT_TRUE(torch::allclose(
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BCEWithLogitsLoss(BCEWithLogitsLossOptions().reduction(torch::kNone))(
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output, target),
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BCELoss(BCELossOptions().reduction(torch::kNone))(
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sigmoid(output), target)));
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ASSERT_TRUE(torch::allclose(
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BCEWithLogitsLoss(BCEWithLogitsLossOptions().reduction(torch::kNone))(
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output, target),
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BCELoss(BCELossOptions().reduction(torch::kNone))(
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sigmoid(output), target)));
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weight = torch::rand({1}, torch::kFloat);
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ASSERT_TRUE(torch::allclose(
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BCEWithLogitsLoss(BCEWithLogitsLossOptions().weight(weight))(
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output, target),
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BCELoss(BCELossOptions().weight(weight))(sigmoid(output), target)));
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}
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{ // test BCE with logits has correct grad at zero
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const auto output = torch::zeros({3, 1}, torch::requires_grad());
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const auto target = torch::zeros({3, 1});
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BCEWithLogitsLoss(BCEWithLogitsLossOptions().reduction(torch::kSum))(
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output, target)
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.backward();
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const auto expected_grad = torch::empty({3, 1}).fill_(0.5);
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ASSERT_TRUE(torch::allclose(output.grad(), expected_grad));
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}
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{ // test BCE with logits broadcasts weights
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const auto target = torch::rand({16, 4});
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const auto output = torch::rand({16, 4}) - 0.5;
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weight = torch::rand({1}, torch::kFloat);
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ASSERT_TRUE(torch::allclose(
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BCEWithLogitsLoss(BCEWithLogitsLossOptions().weight(weight))(
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output, target),
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BCELoss(BCELossOptions().weight(weight))(sigmoid(output), target)));
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}
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{ // test BCE with logits has correct grad at zero
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const auto output = torch::zeros({3, 1}, torch::requires_grad());
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const auto target = torch::zeros({3, 1});
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BCEWithLogitsLoss(BCEWithLogitsLossOptions().reduction(torch::kSum))(
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output, target)
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.backward();
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const auto expected_grad = torch::empty({3, 1}).fill_(0.5);
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ASSERT_TRUE(torch::allclose(output.grad(), expected_grad));
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}
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{ // test BCE with logits broadcasts weights
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const auto target = torch::rand({16, 4});
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const auto output = torch::rand({16, 4}) - 0.5;
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auto weight = torch::rand(4);
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auto out1 = BCEWithLogitsLoss(BCEWithLogitsLossOptions().weight(weight))(
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output, target);
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auto weight = torch::rand(4);
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auto out1 = BCEWithLogitsLoss(BCEWithLogitsLossOptions().weight(weight))(
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output, target);
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weight = weight.expand({16, 4}).contiguous();
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auto out2 = BCEWithLogitsLoss(BCEWithLogitsLossOptions().weight(weight))(
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output, target);
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weight = weight.expand({16, 4}).contiguous();
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auto out2 = BCEWithLogitsLoss(BCEWithLogitsLossOptions().weight(weight))(
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output, target);
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ASSERT_TRUE(torch::allclose(out1, out2));
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ASSERT_TRUE(torch::allclose(out1, out2));
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weight = torch::rand({16, 1});
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out1 = BCEWithLogitsLoss(BCEWithLogitsLossOptions().weight(weight))(
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output, target);
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weight = torch::rand({16, 1});
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out1 = BCEWithLogitsLoss(BCEWithLogitsLossOptions().weight(weight))(
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output, target);
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weight = weight.expand({16, 4}).contiguous();
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out2 = BCEWithLogitsLoss(BCEWithLogitsLossOptions().weight(weight))(
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output, target);
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weight = weight.expand({16, 4}).contiguous();
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out2 = BCEWithLogitsLoss(BCEWithLogitsLossOptions().weight(weight))(
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output, target);
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ASSERT_TRUE(torch::allclose(out1, out2));
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}
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{ // test BCE with logits ones in pos weights are the same as none
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const auto target = torch::rand({64, 4});
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const auto output = torch::rand({64, 4}) - 0.5;
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const auto pos_weight = torch::ones({64, 4});
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ASSERT_TRUE(torch::allclose(out1, out2));
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}
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{ // test BCE with logits ones in pos weights are the same as none
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const auto target = torch::rand({64, 4});
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const auto output = torch::rand({64, 4}) - 0.5;
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const auto pos_weight = torch::ones({64, 4});
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ASSERT_TRUE(torch::allclose(
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BCEWithLogitsLoss()(output, target),
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BCEWithLogitsLoss(BCEWithLogitsLossOptions().pos_weight(pos_weight))(
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output, target)));
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}
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{ // test BCE with logits broadcasts pos weights
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const auto target = torch::rand({64, 4});
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const auto output = torch::rand({64, 4}) - 0.5;
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const auto pos_weight = torch::rand(4);
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const auto out1 = BCEWithLogitsLoss(
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BCEWithLogitsLossOptions().pos_weight(pos_weight))(output, target);
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ASSERT_TRUE(torch::allclose(
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BCEWithLogitsLoss()(output, target),
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BCEWithLogitsLoss(BCEWithLogitsLossOptions().pos_weight(pos_weight))(
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output, target)));
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}
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{ // test BCE with logits broadcasts pos weights
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const auto target = torch::rand({64, 4});
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const auto output = torch::rand({64, 4}) - 0.5;
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const auto pos_weight = torch::rand(4);
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const auto out1 = BCEWithLogitsLoss(
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BCEWithLogitsLossOptions().pos_weight(pos_weight))(output, target);
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const auto pos_weight1 = pos_weight.expand({1, 4});
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const auto out2 = BCEWithLogitsLoss(
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BCEWithLogitsLossOptions().pos_weight(pos_weight))(output, target);
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const auto pos_weight1 = pos_weight.expand({1, 4});
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const auto out2 = BCEWithLogitsLoss(
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BCEWithLogitsLossOptions().pos_weight(pos_weight))(output, target);
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const auto pos_weight2 = pos_weight.expand({64, 4});
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const auto out3 = BCEWithLogitsLoss(
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BCEWithLogitsLossOptions().pos_weight(pos_weight))(output, target);
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const auto pos_weight2 = pos_weight.expand({64, 4});
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const auto out3 = BCEWithLogitsLoss(
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BCEWithLogitsLossOptions().pos_weight(pos_weight))(output, target);
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ASSERT_TRUE(torch::allclose(out1, out2));
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ASSERT_TRUE(torch::allclose(out1, out3));
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}
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{ // test BCE with logits with pos weight has correct grad at zero
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const auto output = torch::zeros({3, 1}, torch::requires_grad());
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const auto target = torch::zeros({3, 1});
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const auto pos_weight = torch::ones({3, 1});
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BCEWithLogitsLoss(
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BCEWithLogitsLossOptions().pos_weight(pos_weight).reduction(torch::kSum))(
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output, target)
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.backward();
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const auto expected_grad = torch::empty({3, 1}).fill_(0.5);
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// NOLINTNEXTLINE(performance-unnecessary-copy-initialization)
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const auto grad = output.grad();
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ASSERT_TRUE(torch::allclose(grad, expected_grad));
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}
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{ // test BCE with logits stability
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const auto output = torch::tensor({0., -120.});
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const auto target = torch::tensor({0., 1.});
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const auto pos_weight = torch::tensor({1., 1.});
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ASSERT_TRUE(torch::allclose(out1, out2));
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ASSERT_TRUE(torch::allclose(out1, out3));
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}
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{ // test BCE with logits with pos weight has correct grad at zero
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const auto output = torch::zeros({3, 1}, torch::requires_grad());
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const auto target = torch::zeros({3, 1});
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const auto pos_weight = torch::ones({3, 1});
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BCEWithLogitsLoss(BCEWithLogitsLossOptions()
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.pos_weight(pos_weight)
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.reduction(torch::kSum))(output, target)
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.backward();
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const auto expected_grad = torch::empty({3, 1}).fill_(0.5);
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// NOLINTNEXTLINE(performance-unnecessary-copy-initialization)
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const auto grad = output.grad();
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ASSERT_TRUE(torch::allclose(grad, expected_grad));
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}
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{ // test BCE with logits stability
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const auto output = torch::tensor({0., -120.});
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const auto target = torch::tensor({0., 1.});
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const auto pos_weight = torch::tensor({1., 1.});
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const auto out1 = BCEWithLogitsLoss()(output, target);
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ASSERT_TRUE(torch::isfinite(out1).all().item<bool>());
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const auto out1 = BCEWithLogitsLoss()(output, target);
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ASSERT_TRUE(torch::isfinite(out1).all().item<bool>());
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const auto out2 = BCEWithLogitsLoss(
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BCEWithLogitsLossOptions().pos_weight(pos_weight))(output, target);
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ASSERT_TRUE(torch::isfinite(out2).all().item<bool>());
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}
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const auto out2 = BCEWithLogitsLoss(
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BCEWithLogitsLossOptions().pos_weight(pos_weight))(output, target);
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ASSERT_TRUE(torch::isfinite(out2).all().item<bool>());
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}
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}
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namespace detail {
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