24241ed6e3
This helps reduce the memory copying needed for dense data. In addition, it helps reduce memory usage even if external memory is not used. - Decouple the number of symbols needed in the compressor with the number of features when the data is dense. - Remove the fetch call in the `at_end_` iteration. - Reduce synchronization and kernel launches by using the `uvector` and ctx.
68 lines
2.8 KiB
C++
68 lines
2.8 KiB
C++
/**
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* Copyright 2021-2024, XGBoost Contributors
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*/
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#include "gradient_index_page_source.h"
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#include <memory> // for make_shared
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#include <utility> // for move
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#include "../common/hist_util.h" // for HistogramCuts
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#include "gradient_index.h" // for GHistIndexMatrix
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namespace xgboost::data {
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void GradientIndexPageSource::Fetch() {
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if (!this->ReadCache()) {
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// source is initialized to be the 0th page during construction, so when count_ is 0
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// there's no need to increment the source.
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if (this->count_ != 0 && !this->sync_) {
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// The mixin doesn't sync the source if `sync_` is false, we need to sync it
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// ourselves.
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++(*source_);
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}
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// This is not read from cache so we still need it to be synced with sparse page source.
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CHECK_EQ(this->count_, this->source_->Iter());
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auto const& csr = this->source_->Page();
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CHECK_NE(this->cuts_.Values().size(), 0);
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this->page_.reset(new GHistIndexMatrix{*csr, feature_types_, cuts_, max_bin_per_feat_,
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is_dense_, sparse_thresh_, nthreads_});
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this->WriteCache();
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}
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}
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void ExtGradientIndexPageSource::Fetch() {
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if (!this->ReadCache()) {
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CHECK_EQ(count_, source_->Iter());
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++(*source_);
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CHECK_GE(source_->Iter(), 1);
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CHECK_NE(cuts_.Values().size(), 0);
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HostAdapterDispatch(proxy_, [this](auto const& value) {
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CHECK(this->proxy_->Ctx()->IsCPU()) << "All batches must use the same device type.";
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// This does three things:
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// - Generate CSR matrix for gradient index.
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// - Generate the column matrix for gradient index.
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// - Concatenate the meta info.
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common::HistogramCuts cuts{this->cuts_};
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this->page_.reset();
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// The external iterator has the data when the `next` method is called. Therefore,
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// it's one step ahead of this source.
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// FIXME(jiamingy): For now, we use the `info->IsDense()` to represent all batches
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// similar to the sparse DMatrix source. We should use per-batch property with proxy
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// DMatrix info instead. This requires more fine-grained tests.
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this->page_ = std::make_shared<GHistIndexMatrix>(
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value.NumRows(), this->base_rows_.at(source_->Iter() - 1), std::move(cuts),
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this->p_.max_bin, info_->IsDense());
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bst_idx_t prev_sum = 0;
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bst_idx_t rbegin = 0;
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// Use `value.NumRows()` for the size of a single batch. Unlike the
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// `IterativeDMatrix`, external memory doesn't concatenate the pages.
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this->page_->PushAdapterBatch(ctx_, rbegin, prev_sum, value, this->missing_,
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this->feature_types_, this->p_.sparse_thresh, value.NumRows());
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this->page_->PushAdapterBatchColumns(ctx_, value, this->missing_, rbegin);
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this->info_->Extend(proxy_->Info(), false, false);
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});
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this->WriteCache();
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}
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}
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} // namespace xgboost::data
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