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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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22
tests/cpp/objective/test_regression_obj.cc
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@@ -122,8 +122,8 @@ TEST(Objective, DeclareUnifiedTest(LogisticRegressionBasic)) {
EXPECT_NEAR(obj->ProbToMargin(0.1f), -2.197f, 0.01f);
EXPECT_NEAR(obj->ProbToMargin(0.5f), 0, 0.01f);
EXPECT_NEAR(obj->ProbToMargin(0.9f), 2.197f, 0.01f);
EXPECT_ANY_THROW(obj->ProbToMargin(10))
<< "Expected error when base_score not in range [0,1f] for LogisticRegression";
EXPECT_ANY_THROW((void)obj->ProbToMargin(10))
<< "Expected error when base_score not in range [0,1f] for LogisticRegression";
// test PredTransform
HostDeviceVector<bst_float> io_preds = {0, 0.1f, 0.5f, 0.9f, 1};
@@ -282,9 +282,9 @@ TEST(Objective, DeclareUnifiedTest(TweedieRegressionGPair)) {
TEST(Objective, CPU_vs_CUDA) {
Context ctx = MakeCUDACtx(GPUIDX);
ObjFunction* obj = ObjFunction::Create("reg:squarederror", &ctx);
HostDeviceVector<GradientPair> cpu_out_preds;
HostDeviceVector<GradientPair> cuda_out_preds;
std::unique_ptr<ObjFunction> obj{ObjFunction::Create("reg:squarederror", &ctx)};
linalg::Matrix<GradientPair> cpu_out_preds;
linalg::Matrix<GradientPair> cuda_out_preds;
constexpr size_t kRows = 400;
constexpr size_t kCols = 100;
@@ -300,7 +300,7 @@ TEST(Objective, CPU_vs_CUDA) {
info.labels.Reshape(kRows);
auto& h_labels = info.labels.Data()->HostVector();
for (size_t i = 0; i < h_labels.size(); ++i) {
h_labels[i] = 1 / (float)(i+1);
h_labels[i] = 1 / static_cast<float>(i+1);
}
{
@@ -314,19 +314,17 @@ TEST(Objective, CPU_vs_CUDA) {
obj->GetGradient(preds, info, 0, &cuda_out_preds);
}
auto& h_cpu_out = cpu_out_preds.HostVector();
auto& h_cuda_out = cuda_out_preds.HostVector();
auto h_cpu_out = cpu_out_preds.HostView();
auto h_cuda_out = cuda_out_preds.HostView();
float sgrad = 0;
float shess = 0;
for (size_t i = 0; i < kRows; ++i) {
sgrad += std::pow(h_cpu_out[i].GetGrad() - h_cuda_out[i].GetGrad(), 2);
shess += std::pow(h_cpu_out[i].GetHess() - h_cuda_out[i].GetHess(), 2);
sgrad += std::pow(h_cpu_out(i).GetGrad() - h_cuda_out(i).GetGrad(), 2);
shess += std::pow(h_cpu_out(i).GetHess() - h_cuda_out(i).GetHess(), 2);
}
ASSERT_NEAR(sgrad, 0.0f, kRtEps);
ASSERT_NEAR(shess, 0.0f, kRtEps);
delete obj;
}
#endif