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fixed lambda indents to be more consistent #13

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Merged
codethinki merged 1 commit into from
Feb 24, 2026
Merged

fixed lambda indents to be more consistent #13

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47 changes: 30 additions & 17 deletions flashlight/fl/test/autograd/AutogradNormalizationTest.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -70,8 +70,10 @@ TEST(AutogradNormalizationTest, BatchNormEvalModeOutputSingleAxis) {

auto expectedOut = (thisInput - thisMean) / std::sqrt(thisVar + 1E-5);
expectedOut = expectedOut * thisWeight + thisBias;
ASSERT_TRUE(allClose(
out.tensor()(sel[0], sel[1], sel[2], sel[3]), expectedOut, 1E-5));
ASSERT_TRUE(
allClose(
out.tensor()(sel[0], sel[1], sel[2], sel[3]), expectedOut, 1E-5)
);
}

// test on empty weigts and bias
Expand All @@ -93,8 +95,10 @@ TEST(AutogradNormalizationTest, BatchNormEvalModeOutputSingleAxis) {
auto thisVar = runningVar.tensor().flatten()(i).scalar<float>();

auto expectedOut = (thisInput - thisMean) / std::sqrt(thisVar + 1E-5);
ASSERT_TRUE(allClose(
out.tensor()(sel[0], sel[1], sel[2], sel[3]), expectedOut, 1E-5));
ASSERT_TRUE(
allClose(
out.tensor()(sel[0], sel[1], sel[2], sel[3]), expectedOut, 1E-5)
);
}
}

Expand Down Expand Up @@ -123,9 +127,7 @@ TEST(AutogradNormalizationTest, BatchNormEvalModeOutputMultipleAxis) {
1E-5
));
for(int i = 0; i < nfeatures; ++i) {
std::array<fl::Index, 4> sel = {
i % 13, (i / 13) % 13, (i / 13) / 13, fl::span
};
std::array<fl::Index, 4> sel = {i % 13, (i / 13) % 13, (i / 13) / 13, fl::span};
auto thisInput = input.tensor()(sel[0], sel[1], sel[2], sel[3]);
auto thisMean = runningMean.tensor().flatten()(i).scalar<float>();
auto thisVar = runningVar.tensor().flatten()(i).scalar<float>();
Expand All @@ -135,8 +137,10 @@ TEST(AutogradNormalizationTest, BatchNormEvalModeOutputMultipleAxis) {
auto expectedOut = (thisInput - thisMean) / std::sqrt(thisVar + 1e-5);
expectedOut = expectedOut * thisWeight + thisBias;

ASSERT_TRUE(allClose(
out.tensor()(sel[0], sel[1], sel[2], sel[3]), expectedOut, 1e-4));
ASSERT_TRUE(
allClose(
out.tensor()(sel[0], sel[1], sel[2], sel[3]), expectedOut, 1e-4)
);
}

// test on empty weigts and bias
Expand All @@ -153,15 +157,20 @@ TEST(AutogradNormalizationTest, BatchNormEvalModeOutputMultipleAxis) {
));
for(int i = 0; i < nfeatures; ++i) {
std::array<fl::Index, 4> sel = {
i % 13, (i / 13) % 13, (i / 13) / 13, fl::span
i % 13,
(i / 13) % 13,
(i / 13) / 13,
fl::span
};
auto thisInput = input.tensor()(sel[0], sel[1], sel[2], sel[3]);
auto thisMean = runningMean.tensor().flatten()(i).scalar<float>();
auto thisVar = runningVar.tensor().flatten()(i).scalar<float>();

auto expectedOut = (thisInput - thisMean) / std::sqrt(thisVar + 1e-5);
ASSERT_TRUE(allClose(
out.tensor()(sel[0], sel[1], sel[2], sel[3]), expectedOut, 5e-5));
ASSERT_TRUE(
allClose(
out.tensor()(sel[0], sel[1], sel[2], sel[3]), expectedOut, 5e-5)
);
}
}

Expand Down Expand Up @@ -230,7 +239,10 @@ TEST(AutogradNormalizationTest, BatchNormTrainModeOutputMultipleAxis) {

for(int i = 0; i < nfeatures; ++i) {
std::array<fl::Index, 4> sel = {
i % 13, (i / 13) % 13, (i / 13) / 13, fl::span
i % 13,
(i / 13) % 13,
(i / 13) / 13,
fl::span
};
auto thisInput = input.tensor()(sel[0], sel[1], sel[2], sel[3]);
auto thisMean = avg.tensor().flatten()(i).scalar<float>();
Expand All @@ -240,8 +252,10 @@ TEST(AutogradNormalizationTest, BatchNormTrainModeOutputMultipleAxis) {

auto expectedOut = (thisInput - thisMean) / std::sqrt(thisVar + 1e-5);
expectedOut = expectedOut * thisWeight + thisBias;
ASSERT_TRUE(allClose(
out.tensor()(sel[0], sel[1], sel[2], sel[3]), expectedOut, 1e-5));
ASSERT_TRUE(
allClose(
out.tensor()(sel[0], sel[1], sel[2], sel[3]), expectedOut, 1e-5)
);
}
}

Expand Down Expand Up @@ -528,8 +542,7 @@ TEST_F(AutogradTestF16, LayerNormJacobianF16) {

std::vector<int> featAxes = {0, 1, 2, 3};
const float inputScale = 4.0; // scale the input to prevent grad underflow
auto input =
Variable(inputScale * fl::rand({2, 2, 2, 4}, fl::dtype::f16), true);
auto input = Variable(inputScale * fl::rand({2, 2, 2, 4}, fl::dtype::f16), true);
auto nfeatures = 1;
for(auto ax : featAxes)
nfeatures *= input.dim(ax);
Expand Down
1 change: 1 addition & 0 deletions uncrustify.cfg
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Original file line number Diff line number Diff line change
Expand Up @@ -26,6 +26,7 @@ indent_func_class_param = false
indent_func_ctor_var_param = false
indent_template_param = false
indent_cpp_lambda_body = true
indent_cpp_lambda_only_once = true
use_indent_func_call_param = true
donot_indent_func_def_close_paren = true
align_func_params = false
Expand Down
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