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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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36
tests/cpp/test_multi_target.cc
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@@ -68,10 +68,12 @@ class TestL1MultiTarget : public ::testing::Test {
}
}
void RunTest(std::string const& tree_method, bool weight) {
void RunTest(Context const* ctx, std::string const& tree_method, bool weight) {
auto p_fmat = weight ? Xyw_ : Xy_;
std::unique_ptr<Learner> learner{Learner::Create({p_fmat})};
learner->SetParams(Args{{"tree_method", tree_method}, {"objective", "reg:absoluteerror"}});
learner->SetParams(Args{{"tree_method", tree_method},
{"objective", "reg:absoluteerror"},
{"device", ctx->DeviceName()}});
learner->Configure();
for (auto i = 0; i < 4; ++i) {
learner->UpdateOneIter(i, p_fmat);
@@ -87,7 +89,9 @@ class TestL1MultiTarget : public ::testing::Test {
for (bst_target_t t{0}; t < p_fmat->Info().labels.Shape(1); ++t) {
auto t_Xy = weight ? single_w_[t] : single_[t];
std::unique_ptr<Learner> sl{Learner::Create({t_Xy})};
sl->SetParams(Args{{"tree_method", tree_method}, {"objective", "reg:absoluteerror"}});
sl->SetParams(Args{{"tree_method", tree_method},
{"objective", "reg:absoluteerror"},
{"device", ctx->DeviceName()}});
sl->Configure();
sl->UpdateOneIter(0, t_Xy);
Json s_config{Object{}};
@@ -104,20 +108,32 @@ class TestL1MultiTarget : public ::testing::Test {
ASSERT_FLOAT_EQ(mean, base_score);
}
void RunTest(std::string const& tree_method) {
this->RunTest(tree_method, false);
this->RunTest(tree_method, true);
void RunTest(Context const* ctx, std::string const& tree_method) {
this->RunTest(ctx, tree_method, false);
this->RunTest(ctx, tree_method, true);
}
};
TEST_F(TestL1MultiTarget, Hist) { this->RunTest("hist"); }
TEST_F(TestL1MultiTarget, Hist) {
Context ctx;
this->RunTest(&ctx, "hist");
}
TEST_F(TestL1MultiTarget, Exact) { this->RunTest("exact"); }
TEST_F(TestL1MultiTarget, Exact) {
Context ctx;
this->RunTest(&ctx, "exact");
}
TEST_F(TestL1MultiTarget, Approx) { this->RunTest("approx"); }
TEST_F(TestL1MultiTarget, Approx) {
Context ctx;
this->RunTest(&ctx, "approx");
}
#if defined(XGBOOST_USE_CUDA)
TEST_F(TestL1MultiTarget, GpuHist) { this->RunTest("gpu_hist"); }
TEST_F(TestL1MultiTarget, GpuHist) {
auto ctx = MakeCUDACtx(0);
this->RunTest(&ctx, "hist");
}
#endif // defined(XGBOOST_USE_CUDA)
TEST(MultiStrategy, Configure) {