Reduce base margin to 2 dim for now. (#7455)
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Jiaming Yuan
GitHub
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bf7bb575b4
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557ffc4bf5
@ -69,7 +69,7 @@ class MetaInfo {
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* if specified, xgboost will start from this init margin
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* can be used to specify initial prediction to boost from.
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*/
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linalg::Tensor<float, 3> base_margin_; // NOLINT
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linalg::Tensor<float, 2> base_margin_; // NOLINT
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/*!
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* \brief lower bound of the label, to be used for survival analysis (censored regression)
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*/
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@ -185,20 +185,20 @@ void MetaInfo::Clear() {
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/*
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* Binary serialization format for MetaInfo:
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*
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* | name | type | is_scalar | num_row | num_col | dim3 | value |
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* |--------------------+----------+-----------+-------------+-------------+-------------+------------------------|
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* | num_row | kUInt64 | True | NA | NA | NA | ${num_row_} |
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* | num_col | kUInt64 | True | NA | NA | NA | ${num_col_} |
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* | num_nonzero | kUInt64 | True | NA | NA | NA | ${num_nonzero_} |
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* | labels | kFloat32 | False | ${size} | 1 | NA | ${labels_} |
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* | group_ptr | kUInt32 | False | ${size} | 1 | NA | ${group_ptr_} |
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* | weights | kFloat32 | False | ${size} | 1 | NA | ${weights_} |
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* | base_margin | kFloat32 | False | ${Shape(0)} | ${Shape(1)} | ${Shape(2)} | ${base_margin_} |
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* | labels_lower_bound | kFloat32 | False | ${size} | 1 | NA | ${labels_lower_bound_} |
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* | labels_upper_bound | kFloat32 | False | ${size} | 1 | NA | ${labels_upper_bound_} |
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* | feature_names | kStr | False | ${size} | 1 | NA | ${feature_names} |
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* | feature_types | kStr | False | ${size} | 1 | NA | ${feature_types} |
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* | feature_types | kFloat32 | False | ${size} | 1 | NA | ${feature_weights} |
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* | name | type | is_scalar | num_row | num_col | value |
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* |--------------------+----------+-----------+-------------+-------------+------------------------|
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* | num_row | kUInt64 | True | NA | NA | ${num_row_} |
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* | num_col | kUInt64 | True | NA | NA | ${num_col_} |
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* | num_nonzero | kUInt64 | True | NA | NA | ${num_nonzero_} |
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* | labels | kFloat32 | False | ${size} | 1 | ${labels_} |
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* | group_ptr | kUInt32 | False | ${size} | 1 | ${group_ptr_} |
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* | weights | kFloat32 | False | ${size} | 1 | ${weights_} |
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* | base_margin | kFloat32 | False | ${Shape(0)} | ${Shape(1)} | ${base_margin_} |
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* | labels_lower_bound | kFloat32 | False | ${size} | 1 | ${labels_lower_bound_} |
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* | labels_upper_bound | kFloat32 | False | ${size} | 1 | ${labels_upper_bound_} |
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* | feature_names | kStr | False | ${size} | 1 | ${feature_names} |
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* | feature_types | kStr | False | ${size} | 1 | ${feature_types} |
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* | feature_weights | kFloat32 | False | ${size} | 1 | ${feature_weights} |
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*
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* Note that the scalar fields (is_scalar=True) will have num_row and num_col missing.
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* Also notice the difference between the saved name and the name used in `SetInfo':
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@ -344,7 +344,7 @@ MetaInfo MetaInfo::Slice(common::Span<int32_t const> ridxs) const {
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CHECK_EQ(this->base_margin_.Size() % this->num_row_, 0)
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<< "Incorrect size of base margin vector.";
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auto margin = this->base_margin_.View(this->base_margin_.Data()->DeviceIdx());
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out.base_margin_.Reshape(ridxs.size(), margin.Shape()[1], margin.Shape()[2]);
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out.base_margin_.Reshape(ridxs.size(), margin.Shape()[1]);
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size_t stride = margin.Stride(0);
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out.base_margin_.Data()->HostVector() =
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Gather(this->base_margin_.Data()->HostVector(), ridxs, stride);
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@ -447,7 +447,7 @@ void MetaInfo::SetInfo(StringView key, StringView interface_str) {
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void MetaInfo::SetInfoFromHost(StringView key, Json arr) {
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// multi-dim float info
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if (key == "base_margin") {
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CopyTensorInfoImpl<3>(arr, &this->base_margin_);
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CopyTensorInfoImpl(arr, &this->base_margin_);
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// FIXME(jiamingy): Remove the deprecated API and let all language bindings aware of
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// input shape. This issue is CPU only since CUDA uses array interface from day 1.
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//
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@ -137,10 +137,10 @@ SimpleDMatrix::SimpleDMatrix(AdapterT* adapter, float missing, int nthread) {
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batch.Weights() + batch.Size());
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}
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if (batch.BaseMargin() != nullptr) {
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info_.base_margin_ = linalg::Tensor<float, 3>{batch.BaseMargin(),
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batch.BaseMargin() + batch.Size(),
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{batch.Size()},
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GenericParameter::kCpuId};
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info_.base_margin_ = decltype(info_.base_margin_){batch.BaseMargin(),
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batch.BaseMargin() + batch.Size(),
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{batch.Size()},
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GenericParameter::kCpuId};
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}
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if (batch.Qid() != nullptr) {
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qids.insert(qids.end(), batch.Qid(), batch.Qid() + batch.Size());
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@ -61,7 +61,8 @@ Predictor* Predictor::Create(
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return p_predictor;
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}
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void ValidateBaseMarginShape(linalg::Tensor<float, 3> const& margin, bst_row_t n_samples,
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template <int32_t D>
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void ValidateBaseMarginShape(linalg::Tensor<float, D> const& margin, bst_row_t n_samples,
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bst_group_t n_groups) {
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// FIXME: Bindings other than Python doesn't have shape.
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std::string expected{"Invalid shape of base_margin. Expected: (" + std::to_string(n_samples) +
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@ -55,24 +55,23 @@ inline void TestMetaInfoStridedData(int32_t device) {
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}
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{
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// base margin
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linalg::Tensor<float, 4> base_margin;
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base_margin.Reshape(4, 3, 2, 3);
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linalg::Tensor<float, 3> base_margin;
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base_margin.Reshape(4, 2, 3);
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auto& h_margin = base_margin.Data()->HostVector();
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std::iota(h_margin.begin(), h_margin.end(), 0.0);
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auto t_margin = base_margin.View(device).Slice(linalg::All(), linalg::All(), 0, linalg::All());
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ASSERT_EQ(t_margin.Shape().size(), 3);
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auto t_margin = base_margin.View(device).Slice(linalg::All(), 0, linalg::All());
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ASSERT_EQ(t_margin.Shape().size(), 2);
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info.SetInfo("base_margin", StringView{t_margin.ArrayInterfaceStr()});
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auto const& h_result = info.base_margin_.View(-1);
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ASSERT_EQ(h_result.Shape().size(), 3);
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ASSERT_EQ(h_result.Shape().size(), 2);
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auto in_margin = base_margin.View(-1);
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linalg::ElementWiseKernelHost(h_result, omp_get_max_threads(), [&](size_t i, float v_0) {
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auto tup = linalg::UnravelIndex(i, h_result.Shape());
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auto i0 = std::get<0>(tup);
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auto i1 = std::get<1>(tup);
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auto i2 = std::get<2>(tup);
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// Sliced at 3^th dimension.
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auto v_1 = in_margin(i0, i1, 0, i2);
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// Sliced at second dimension.
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auto v_1 = in_margin(i0, 0, i1);
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CHECK_EQ(v_0, v_1);
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return v_0;
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});
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@ -254,7 +254,7 @@ TEST(SimpleDMatrix, Slice) {
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std::iota(upper.begin(), upper.end(), 1.0f);
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auto& margin = p_m->Info().base_margin_;
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margin = linalg::Tensor<float, 3>{{kRows, kClasses}, GenericParameter::kCpuId};
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margin = decltype(p_m->Info().base_margin_){{kRows, kClasses}, GenericParameter::kCpuId};
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std::array<int32_t, 3> ridxs {1, 3, 5};
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std::unique_ptr<DMatrix> out { p_m->Slice(ridxs) };
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@ -108,8 +108,8 @@ TEST(GPUPredictor, ExternalMemoryTest) {
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dmats.push_back(CreateSparsePageDMatrix(8000));
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for (const auto& dmat: dmats) {
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dmat->Info().base_margin_ =
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linalg::Tensor<float, 3>{{dmat->Info().num_row_, static_cast<size_t>(n_classes)}, 0};
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dmat->Info().base_margin_ = decltype(dmat->Info().base_margin_){
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{dmat->Info().num_row_, static_cast<size_t>(n_classes)}, 0};
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dmat->Info().base_margin_.Data()->Fill(0.5);
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PredictionCacheEntry out_predictions;
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gpu_predictor->InitOutPredictions(dmat->Info(), &out_predictions.predictions, model);
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