/
iterative_dmatrix.cu
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/
iterative_dmatrix.cu
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/*!
* Copyright 2020-2022 XGBoost contributors
*/
#include <algorithm>
#include <memory>
#include <type_traits>
#include "../common/hist_util.cuh"
#include "device_adapter.cuh"
#include "ellpack_page.cuh"
#include "iterative_dmatrix.h"
#include "proxy_dmatrix.cuh"
#include "proxy_dmatrix.h"
#include "simple_batch_iterator.h"
#include "sparse_page_source.h"
namespace xgboost {
namespace data {
void IterativeDMatrix::InitFromCUDA(DataIterHandle iter_handle, float missing,
std::shared_ptr<DMatrix> ref) {
// A handle passed to external iterator.
DMatrixProxy* proxy = MakeProxy(proxy_);
CHECK(proxy);
// The external iterator
auto iter =
DataIterProxy<DataIterResetCallback, XGDMatrixCallbackNext>{iter_handle, reset_, next_};
dh::XGBCachingDeviceAllocator<char> alloc;
auto num_rows = [&]() {
return Dispatch(proxy, [](auto const& value) { return value.NumRows(); });
};
auto num_cols = [&]() {
return Dispatch(proxy, [](auto const& value) { return value.NumCols(); });
};
size_t row_stride = 0;
size_t nnz = 0;
// Sketch for all batches.
std::vector<common::SketchContainer> sketch_containers;
size_t batches = 0;
size_t accumulated_rows = 0;
bst_feature_t cols = 0;
int32_t current_device;
dh::safe_cuda(cudaGetDevice(¤t_device));
auto get_device = [&]() -> int32_t {
int32_t d = (ctx_.gpu_id == Context::kCpuId) ? current_device : ctx_.gpu_id;
CHECK_NE(d, Context::kCpuId);
return d;
};
/**
* Generate quantiles
*/
common::HistogramCuts cuts;
do {
// We use do while here as the first batch is fetched in ctor
ctx_.gpu_id = proxy->DeviceIdx();
CHECK_LT(ctx_.gpu_id, common::AllVisibleGPUs());
dh::safe_cuda(cudaSetDevice(get_device()));
if (cols == 0) {
cols = num_cols();
rabit::Allreduce<rabit::op::Max>(&cols, 1);
this->info_.num_col_ = cols;
} else {
CHECK_EQ(cols, num_cols()) << "Inconsistent number of columns.";
}
if (!ref) {
sketch_containers.emplace_back(proxy->Info().feature_types, batch_param_.max_bin, cols,
num_rows(), get_device());
auto* p_sketch = &sketch_containers.back();
proxy->Info().weights_.SetDevice(get_device());
Dispatch(proxy, [&](auto const& value) {
common::AdapterDeviceSketch(value, batch_param_.max_bin, proxy->Info(), missing, p_sketch);
});
}
auto batch_rows = num_rows();
accumulated_rows += batch_rows;
dh::caching_device_vector<size_t> row_counts(batch_rows + 1, 0);
common::Span<size_t> row_counts_span(row_counts.data().get(), row_counts.size());
row_stride = std::max(row_stride, Dispatch(proxy, [=](auto const& value) {
return GetRowCounts(value, row_counts_span, get_device(), missing);
}));
nnz += thrust::reduce(thrust::cuda::par(alloc), row_counts.begin(), row_counts.end());
batches++;
} while (iter.Next());
iter.Reset();
dh::safe_cuda(cudaSetDevice(get_device()));
if (!ref) {
HostDeviceVector<FeatureType> ft;
common::SketchContainer final_sketch(
sketch_containers.empty() ? ft : sketch_containers.front().FeatureTypes(),
batch_param_.max_bin, cols, accumulated_rows, get_device());
for (auto const& sketch : sketch_containers) {
final_sketch.Merge(sketch.ColumnsPtr(), sketch.Data());
final_sketch.FixError();
}
sketch_containers.clear();
sketch_containers.shrink_to_fit();
final_sketch.MakeCuts(&cuts);
} else {
GetCutsFromRef(ref, Info().num_col_, batch_param_, &cuts);
}
this->info_.num_row_ = accumulated_rows;
this->info_.num_nonzero_ = nnz;
auto init_page = [this, &proxy, &cuts, row_stride, accumulated_rows, get_device]() {
if (!ellpack_) {
// Should be put inside the while loop to protect against empty batch. In
// that case device id is invalid.
ellpack_.reset(new EllpackPage);
*(ellpack_->Impl()) =
EllpackPageImpl(get_device(), cuts, this->IsDense(), row_stride, accumulated_rows);
}
};
/**
* Generate gradient index.
*/
size_t offset = 0;
iter.Reset();
size_t n_batches_for_verification = 0;
while (iter.Next()) {
init_page();
dh::safe_cuda(cudaSetDevice(get_device()));
auto rows = num_rows();
dh::caching_device_vector<size_t> row_counts(rows + 1, 0);
common::Span<size_t> row_counts_span(row_counts.data().get(), row_counts.size());
Dispatch(proxy, [=](auto const& value) {
return GetRowCounts(value, row_counts_span, get_device(), missing);
});
auto is_dense = this->IsDense();
proxy->Info().feature_types.SetDevice(get_device());
auto d_feature_types = proxy->Info().feature_types.ConstDeviceSpan();
auto new_impl = Dispatch(proxy, [&](auto const& value) {
return EllpackPageImpl(value, missing, get_device(), is_dense, row_counts_span,
d_feature_types, row_stride, rows, cuts);
});
size_t num_elements = ellpack_->Impl()->Copy(get_device(), &new_impl, offset);
offset += num_elements;
proxy->Info().num_row_ = num_rows();
proxy->Info().num_col_ = cols;
if (batches != 1) {
this->info_.Extend(std::move(proxy->Info()), false, true);
}
n_batches_for_verification++;
}
CHECK_EQ(batches, n_batches_for_verification)
<< "Different number of batches returned between 2 iterations";
if (batches == 1) {
this->info_ = std::move(proxy->Info());
this->info_.num_nonzero_ = nnz;
CHECK_EQ(proxy->Info().labels.Size(), 0);
}
iter.Reset();
// Synchronise worker columns
rabit::Allreduce<rabit::op::Max>(&info_.num_col_, 1);
}
BatchSet<EllpackPage> IterativeDMatrix::GetEllpackBatches(BatchParam const& param) {
CheckParam(param);
if (!ellpack_ && !ghist_) {
LOG(FATAL) << "`QuantileDMatrix` not initialized.";
}
if (!ellpack_ && ghist_) {
ellpack_.reset(new EllpackPage());
// Evaluation QuantileDMatrix initialized from CPU data might not have the correct GPU
// ID.
if (this->ctx_.IsCPU()) {
this->ctx_.gpu_id = param.gpu_id;
}
if (this->ctx_.IsCPU()) {
this->ctx_.gpu_id = dh::CurrentDevice();
}
this->Info().feature_types.SetDevice(this->ctx_.gpu_id);
*ellpack_->Impl() =
EllpackPageImpl(&ctx_, *this->ghist_, this->Info().feature_types.ConstDeviceSpan());
}
CHECK(ellpack_);
auto begin_iter = BatchIterator<EllpackPage>(new SimpleBatchIteratorImpl<EllpackPage>(ellpack_));
return BatchSet<EllpackPage>(begin_iter);
}
void GetCutsFromEllpack(EllpackPage const& page, common::HistogramCuts* cuts) {
*cuts = page.Impl()->Cuts();
}
} // namespace data
} // namespace xgboost