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Implement iterative DMatrix for CPU. (#8116)
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,214 @@ | ||
| /*! | ||
| * Copyright 2022 XGBoost contributors | ||
| */ | ||
| #include "iterative_dmatrix.h" | ||
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| #include <rabit/rabit.h> | ||
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| #include "../common/column_matrix.h" | ||
| #include "../common/hist_util.h" | ||
| #include "gradient_index.h" | ||
| #include "proxy_dmatrix.h" | ||
| #include "simple_batch_iterator.h" | ||
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| namespace xgboost { | ||
| namespace data { | ||
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| void GetCutsFromRef(std::shared_ptr<DMatrix> ref_, bst_feature_t n_features, BatchParam p, | ||
| common::HistogramCuts* p_cuts) { | ||
| CHECK(ref_); | ||
| CHECK(p_cuts); | ||
| auto csr = [&]() { | ||
| for (auto const& page : ref_->GetBatches<GHistIndexMatrix>(p)) { | ||
| *p_cuts = page.cut; | ||
| break; | ||
| } | ||
| }; | ||
| auto ellpack = [&]() { | ||
| for (auto const& page : ref_->GetBatches<EllpackPage>(p)) { | ||
| GetCutsFromEllpack(page, p_cuts); | ||
| break; | ||
| } | ||
| }; | ||
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| if (ref_->PageExists<GHistIndexMatrix>()) { | ||
| csr(); | ||
| } else if (ref_->PageExists<EllpackPage>()) { | ||
| ellpack(); | ||
| } else { | ||
| if (p.gpu_id == Context::kCpuId) { | ||
| csr(); | ||
| } else { | ||
| ellpack(); | ||
| } | ||
| } | ||
| CHECK_EQ(ref_->Info().num_col_, n_features) | ||
| << "Invalid ref DMatrix, different number of features."; | ||
| } | ||
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| void IterativeDMatrix::InitFromCPU(DataIterHandle iter_handle, float missing, | ||
| std::shared_ptr<DMatrix> ref) { | ||
| DMatrixProxy* proxy = MakeProxy(proxy_); | ||
| CHECK(proxy); | ||
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| // The external iterator | ||
| auto iter = | ||
| DataIterProxy<DataIterResetCallback, XGDMatrixCallbackNext>{iter_handle, reset_, next_}; | ||
| common::HistogramCuts cuts; | ||
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| auto num_rows = [&]() { | ||
| return HostAdapterDispatch(proxy, [](auto const& value) { return value.Size(); }); | ||
| }; | ||
| auto num_cols = [&]() { | ||
| return HostAdapterDispatch(proxy, [](auto const& value) { return value.NumCols(); }); | ||
| }; | ||
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| std::vector<size_t> column_sizes; | ||
| auto const is_valid = data::IsValidFunctor{missing}; | ||
| auto nnz_cnt = [&]() { | ||
| return HostAdapterDispatch(proxy, [&](auto const& value) { | ||
| size_t n_threads = ctx_.Threads(); | ||
| size_t n_features = column_sizes.size(); | ||
| linalg::Tensor<size_t, 2> column_sizes_tloc({n_threads, n_features}, Context::kCpuId); | ||
| auto view = column_sizes_tloc.HostView(); | ||
| common::ParallelFor(value.Size(), n_threads, common::Sched::Static(256), [&](auto i) { | ||
| auto const& line = value.GetLine(i); | ||
| for (size_t j = 0; j < line.Size(); ++j) { | ||
| data::COOTuple const& elem = line.GetElement(j); | ||
| if (is_valid(elem)) { | ||
| view(omp_get_thread_num(), elem.column_idx)++; | ||
| } | ||
| } | ||
| }); | ||
| auto ptr = column_sizes_tloc.Data()->HostPointer(); | ||
| auto result = std::accumulate(ptr, ptr + column_sizes_tloc.Size(), static_cast<size_t>(0)); | ||
| for (size_t tidx = 0; tidx < n_threads; ++tidx) { | ||
| for (size_t fidx = 0; fidx < n_features; ++fidx) { | ||
| column_sizes[fidx] += view(tidx, fidx); | ||
| } | ||
| } | ||
| return result; | ||
| }); | ||
| }; | ||
|
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| size_t n_features = 0; | ||
| size_t n_batches = 0; | ||
| size_t accumulated_rows{0}; | ||
| size_t nnz{0}; | ||
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| /** | ||
| * CPU impl needs an additional loop for accumulating the column size. | ||
| */ | ||
| std::unique_ptr<common::HostSketchContainer> p_sketch; | ||
| std::vector<size_t> batch_nnz; | ||
| do { | ||
| // We use do while here as the first batch is fetched in ctor | ||
| if (n_features == 0) { | ||
| n_features = num_cols(); | ||
| rabit::Allreduce<rabit::op::Max>(&n_features, 1); | ||
| column_sizes.resize(n_features); | ||
| info_.num_col_ = n_features; | ||
| } else { | ||
| CHECK_EQ(n_features, num_cols()) << "Inconsistent number of columns."; | ||
| } | ||
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| size_t batch_size = num_rows(); | ||
| batch_nnz.push_back(nnz_cnt()); | ||
| nnz += batch_nnz.back(); | ||
| accumulated_rows += batch_size; | ||
| n_batches++; | ||
| } while (iter.Next()); | ||
| iter.Reset(); | ||
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| // From here on Info() has the correct data shape | ||
| Info().num_row_ = accumulated_rows; | ||
| Info().num_nonzero_ = nnz; | ||
| rabit::Allreduce<rabit::op::Max>(&info_.num_col_, 1); | ||
| CHECK(std::none_of(column_sizes.cbegin(), column_sizes.cend(), [&](auto f) { | ||
| return f > accumulated_rows; | ||
| })) << "Something went wrong during iteration."; | ||
|
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| /** | ||
| * Generate quantiles | ||
| */ | ||
| accumulated_rows = 0; | ||
| if (ref) { | ||
| GetCutsFromRef(ref, Info().num_col_, batch_param_, &cuts); | ||
| } else { | ||
| size_t i = 0; | ||
| while (iter.Next()) { | ||
| if (!p_sketch) { | ||
| p_sketch.reset(new common::HostSketchContainer{batch_param_.max_bin, | ||
| proxy->Info().feature_types.ConstHostSpan(), | ||
| column_sizes, false, ctx_.Threads()}); | ||
| } | ||
| HostAdapterDispatch(proxy, [&](auto const& batch) { | ||
| proxy->Info().num_nonzero_ = batch_nnz[i]; | ||
| // We don't need base row idx here as Info is from proxy and the number of rows in | ||
| // it is consistent with data batch. | ||
| p_sketch->PushAdapterBatch(batch, 0, proxy->Info(), missing); | ||
| }); | ||
| accumulated_rows += num_rows(); | ||
| ++i; | ||
| } | ||
| iter.Reset(); | ||
| CHECK_EQ(accumulated_rows, Info().num_row_); | ||
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| CHECK(p_sketch); | ||
| p_sketch->MakeCuts(&cuts); | ||
| } | ||
|
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| /** | ||
| * Generate gradient index. | ||
| */ | ||
| this->ghist_ = std::make_unique<GHistIndexMatrix>(Info(), std::move(cuts), batch_param_.max_bin); | ||
| size_t rbegin = 0; | ||
| size_t prev_sum = 0; | ||
| size_t i = 0; | ||
| while (iter.Next()) { | ||
| HostAdapterDispatch(proxy, [&](auto const& batch) { | ||
| proxy->Info().num_nonzero_ = batch_nnz[i]; | ||
| this->ghist_->PushAdapterBatch(&ctx_, rbegin, prev_sum, batch, missing, | ||
| proxy->Info().feature_types.ConstHostSpan(), | ||
| batch_param_.sparse_thresh, Info().num_row_); | ||
| }); | ||
| if (n_batches != 1) { | ||
| this->info_.Extend(std::move(proxy->Info()), false, true); | ||
| } | ||
| size_t batch_size = num_rows(); | ||
| prev_sum = this->ghist_->row_ptr[rbegin + batch_size]; | ||
| rbegin += batch_size; | ||
| ++i; | ||
| } | ||
| iter.Reset(); | ||
| CHECK_EQ(rbegin, Info().num_row_); | ||
|
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| /** | ||
| * Generate column matrix | ||
| */ | ||
| accumulated_rows = 0; | ||
| while (iter.Next()) { | ||
| HostAdapterDispatch(proxy, [&](auto const& batch) { | ||
| this->ghist_->PushAdapterBatchColumns(&ctx_, batch, missing, accumulated_rows); | ||
| }); | ||
| accumulated_rows += num_rows(); | ||
| } | ||
| iter.Reset(); | ||
| CHECK_EQ(accumulated_rows, Info().num_row_); | ||
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| if (n_batches == 1) { | ||
| this->info_ = std::move(proxy->Info()); | ||
| this->info_.num_nonzero_ = nnz; | ||
| CHECK_EQ(proxy->Info().labels.Size(), 0); | ||
| } | ||
| } | ||
|
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| BatchSet<GHistIndexMatrix> IterativeDMatrix::GetGradientIndex(BatchParam const& param) { | ||
| CheckParam(param); | ||
| CHECK(ghist_) << "Not initialized with CPU data"; | ||
| auto begin_iter = | ||
| BatchIterator<GHistIndexMatrix>(new SimpleBatchIteratorImpl<GHistIndexMatrix>(ghist_)); | ||
| return BatchSet<GHistIndexMatrix>(begin_iter); | ||
| } | ||
| } // namespace data | ||
| } // namespace xgboost |
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