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Use matrix for gradient. (#9508)

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- Use the `linalg::Matrix` for storing gradients.
- New API for the custom objective.
- Custom objective for multi-class/multi-target is now required to return the correct shape.
- Custom objective for Python can accept arrays with any strides. (row-major, column-major)
This commit is contained in:
Jiaming Yuan
2023-08-24 05:29:52 +08:00
committed by GitHub
parent 6103dca0bb
commit 972730cde0
77 changed files with 1052 additions and 651 deletions
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32
tests/cpp/objective/test_lambdarank_obj.cc
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@@ -74,35 +74,35 @@ void TestNDCGGPair(Context const* ctx) {
info.labels = linalg::Tensor<float, 2>{{0, 1, 0, 1}, {4, 1}, GPUIDX};
info.group_ptr_ = {0, 2, 4};
info.num_row_ = 4;
HostDeviceVector<GradientPair> gpairs;
linalg::Matrix<GradientPair> gpairs;
obj->GetGradient(predts, info, 0, &gpairs);
ASSERT_EQ(gpairs.Size(), predts.Size());
{
predts = {1, 0, 1, 0};
HostDeviceVector<GradientPair> gpairs;
linalg::Matrix<GradientPair> gpairs;
obj->GetGradient(predts, info, 0, &gpairs);
for (size_t i = 0; i < gpairs.Size(); ++i) {
ASSERT_GT(gpairs.HostSpan()[i].GetHess(), 0);
for (std::size_t i = 0; i < gpairs.Size(); ++i) {
ASSERT_GT(gpairs.HostView()(i).GetHess(), 0);
}
ASSERT_LT(gpairs.HostSpan()[1].GetGrad(), 0);
ASSERT_LT(gpairs.HostSpan()[3].GetGrad(), 0);
ASSERT_LT(gpairs.HostView()(1).GetGrad(), 0);
ASSERT_LT(gpairs.HostView()(3).GetGrad(), 0);
ASSERT_GT(gpairs.HostSpan()[0].GetGrad(), 0);
ASSERT_GT(gpairs.HostSpan()[2].GetGrad(), 0);
ASSERT_GT(gpairs.HostView()(0).GetGrad(), 0);
ASSERT_GT(gpairs.HostView()(2).GetGrad(), 0);
info.weights_ = {2, 3};
HostDeviceVector<GradientPair> weighted_gpairs;
linalg::Matrix<GradientPair> weighted_gpairs;
obj->GetGradient(predts, info, 0, &weighted_gpairs);
auto const& h_gpairs = gpairs.ConstHostSpan();
auto const& h_weighted_gpairs = weighted_gpairs.ConstHostSpan();
auto const& h_gpairs = gpairs.HostView();
auto const& h_weighted_gpairs = weighted_gpairs.HostView();
for (size_t i : {0ul, 1ul}) {
ASSERT_FLOAT_EQ(h_weighted_gpairs[i].GetGrad(), h_gpairs[i].GetGrad() * 2.0f);
ASSERT_FLOAT_EQ(h_weighted_gpairs[i].GetHess(), h_gpairs[i].GetHess() * 2.0f);
ASSERT_FLOAT_EQ(h_weighted_gpairs(i).GetGrad(), h_gpairs(i).GetGrad() * 2.0f);
ASSERT_FLOAT_EQ(h_weighted_gpairs(i).GetHess(), h_gpairs(i).GetHess() * 2.0f);
}
for (size_t i : {2ul, 3ul}) {
ASSERT_FLOAT_EQ(h_weighted_gpairs[i].GetGrad(), h_gpairs[i].GetGrad() * 3.0f);
ASSERT_FLOAT_EQ(h_weighted_gpairs[i].GetHess(), h_gpairs[i].GetHess() * 3.0f);
ASSERT_FLOAT_EQ(h_weighted_gpairs(i).GetGrad(), h_gpairs(i).GetGrad() * 3.0f);
ASSERT_FLOAT_EQ(h_weighted_gpairs(i).GetHess(), h_gpairs(i).GetHess() * 3.0f);
}
}
@@ -125,7 +125,7 @@ void TestUnbiasedNDCG(Context const* ctx) {
std::sort(h_label.begin(), h_label.end(), std::greater<>{});
HostDeviceVector<float> predt(p_fmat->Info().num_row_, 1.0f);
HostDeviceVector<GradientPair> out_gpair;
linalg::Matrix<GradientPair> out_gpair;
obj->GetGradient(predt, p_fmat->Info(), 0, &out_gpair);
Json config{Object{}};