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Use Booster context in DMatrix. (#8896)

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- Pass context from booster to DMatrix.
- Use context instead of integer for `n_threads`.
- Check the consistency configuration for `max_bin`.
- Test for all combinations of initialization options.
This commit is contained in:
Jiaming Yuan
2023-04-28 21:47:14 +08:00
committed by GitHub
parent 1f9a57d17b
commit 08ce495b5d
67 changed files with 1283 additions and 935 deletions
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88
src/tree/gpu_hist/gradient_based_sampler.cu
View File

@@ -1,5 +1,5 @@
/*!
* Copyright 2019-2021 by XGBoost Contributors
/**
* Copyright 2019-2023 by XGBoost Contributors
*/
#include <thrust/functional.h>
#include <thrust/random.h>
@@ -12,6 +12,7 @@
#include <utility>
#include "../../common/compressed_iterator.h"
#include "../../common/cuda_context.cuh" // for CUDAContext
#include "../../common/random.h"
#include "../param.h"
#include "gradient_based_sampler.cuh"
@@ -147,25 +148,26 @@ class PoissonSampling : public thrust::binary_function<GradientPair, size_t, Gra
NoSampling::NoSampling(EllpackPageImpl const* page) : page_(page) {}
GradientBasedSample NoSampling::Sample(common::Span<GradientPair> gpair, DMatrix* dmat) {
GradientBasedSample NoSampling::Sample(Context const*, common::Span<GradientPair> gpair,
DMatrix* dmat) {
return {dmat->Info().num_row_, page_, gpair};
}
ExternalMemoryNoSampling::ExternalMemoryNoSampling(EllpackPageImpl const* page,
size_t n_rows,
const BatchParam& batch_param)
: batch_param_(batch_param),
page_(new EllpackPageImpl(batch_param.gpu_id, page->Cuts(), page->is_dense,
page->row_stride, n_rows)) {}
ExternalMemoryNoSampling::ExternalMemoryNoSampling(Context const* ctx, EllpackPageImpl const* page,
size_t n_rows, BatchParam batch_param)
: batch_param_{std::move(batch_param)},
page_(new EllpackPageImpl(ctx->gpu_id, page->Cuts(), page->is_dense, page->row_stride,
n_rows)) {}
GradientBasedSample ExternalMemoryNoSampling::Sample(common::Span<GradientPair> gpair,
GradientBasedSample ExternalMemoryNoSampling::Sample(Context const* ctx,
common::Span<GradientPair> gpair,
DMatrix* dmat) {
if (!page_concatenated_) {
// Concatenate all the external memory ELLPACK pages into a single in-memory page.
size_t offset = 0;
for (auto& batch : dmat->GetBatches<EllpackPage>(batch_param_)) {
for (auto& batch : dmat->GetBatches<EllpackPage>(ctx, batch_param_)) {
auto page = batch.Impl();
size_t num_elements = page_->Copy(batch_param_.gpu_id, page, offset);
size_t num_elements = page_->Copy(ctx->gpu_id, page, offset);
offset += num_elements;
}
page_concatenated_ = true;
@@ -176,12 +178,13 @@ GradientBasedSample ExternalMemoryNoSampling::Sample(common::Span<GradientPair>
UniformSampling::UniformSampling(EllpackPageImpl const* page, float subsample)
: page_(page), subsample_(subsample) {}
GradientBasedSample UniformSampling::Sample(common::Span<GradientPair> gpair, DMatrix* dmat) {
GradientBasedSample UniformSampling::Sample(Context const* ctx, common::Span<GradientPair> gpair,
DMatrix* dmat) {
// Set gradient pair to 0 with p = 1 - subsample
thrust::replace_if(dh::tbegin(gpair), dh::tend(gpair),
thrust::counting_iterator<size_t>(0),
BernoulliTrial(common::GlobalRandom()(), subsample_),
GradientPair());
auto cuctx = ctx->CUDACtx();
thrust::replace_if(cuctx->CTP(), dh::tbegin(gpair), dh::tend(gpair),
thrust::counting_iterator<std::size_t>(0),
BernoulliTrial(common::GlobalRandom()(), subsample_), GradientPair());
return {dmat->Info().num_row_, page_, gpair};
}
@@ -192,7 +195,8 @@ ExternalMemoryUniformSampling::ExternalMemoryUniformSampling(size_t n_rows,
subsample_(subsample),
sample_row_index_(n_rows) {}
GradientBasedSample ExternalMemoryUniformSampling::Sample(common::Span<GradientPair> gpair,
GradientBasedSample ExternalMemoryUniformSampling::Sample(Context const* ctx,
common::Span<GradientPair> gpair,
DMatrix* dmat) {
// Set gradient pair to 0 with p = 1 - subsample
thrust::replace_if(dh::tbegin(gpair), dh::tend(gpair),
@@ -216,18 +220,17 @@ GradientBasedSample ExternalMemoryUniformSampling::Sample(common::Span<GradientP
sample_row_index_.begin(),
ClearEmptyRows());
auto batch_iterator = dmat->GetBatches<EllpackPage>(batch_param_);
auto batch_iterator = dmat->GetBatches<EllpackPage>(ctx, batch_param_);
auto first_page = (*batch_iterator.begin()).Impl();
// Create a new ELLPACK page with empty rows.
page_.reset(); // Release the device memory first before reallocating
page_.reset(new EllpackPageImpl(
batch_param_.gpu_id, first_page->Cuts(), first_page->is_dense,
first_page->row_stride, sample_rows));
page_.reset(new EllpackPageImpl(ctx->gpu_id, first_page->Cuts(), first_page->is_dense,
first_page->row_stride, sample_rows));
// Compact the ELLPACK pages into the single sample page.
thrust::fill(dh::tbegin(page_->gidx_buffer), dh::tend(page_->gidx_buffer), 0);
for (auto& batch : batch_iterator) {
page_->Compact(batch_param_.gpu_id, batch.Impl(), dh::ToSpan(sample_row_index_));
page_->Compact(ctx->gpu_id, batch.Impl(), dh::ToSpan(sample_row_index_));
}
return {sample_rows, page_.get(), dh::ToSpan(gpair_)};
@@ -242,18 +245,17 @@ GradientBasedSampling::GradientBasedSampling(EllpackPageImpl const* page,
threshold_(n_rows + 1, 0.0f),
grad_sum_(n_rows, 0.0f) {}
GradientBasedSample GradientBasedSampling::Sample(common::Span<GradientPair> gpair,
DMatrix* dmat) {
GradientBasedSample GradientBasedSampling::Sample(Context const* ctx,
common::Span<GradientPair> gpair, DMatrix* dmat) {
auto cuctx = ctx->CUDACtx();
size_t n_rows = dmat->Info().num_row_;
size_t threshold_index = GradientBasedSampler::CalculateThresholdIndex(
gpair, dh::ToSpan(threshold_), dh::ToSpan(grad_sum_), n_rows * subsample_);
// Perform Poisson sampling in place.
thrust::transform(dh::tbegin(gpair), dh::tend(gpair),
thrust::counting_iterator<size_t>(0),
dh::tbegin(gpair),
PoissonSampling(dh::ToSpan(threshold_),
threshold_index,
thrust::transform(cuctx->CTP(), dh::tbegin(gpair), dh::tend(gpair),
thrust::counting_iterator<size_t>(0), dh::tbegin(gpair),
PoissonSampling(dh::ToSpan(threshold_), threshold_index,
RandomWeight(common::GlobalRandom()())));
return {n_rows, page_, gpair};
}
@@ -268,7 +270,8 @@ ExternalMemoryGradientBasedSampling::ExternalMemoryGradientBasedSampling(
grad_sum_(n_rows, 0.0f),
sample_row_index_(n_rows) {}
GradientBasedSample ExternalMemoryGradientBasedSampling::Sample(common::Span<GradientPair> gpair,
GradientBasedSample ExternalMemoryGradientBasedSampling::Sample(Context const* ctx,
common::Span<GradientPair> gpair,
DMatrix* dmat) {
size_t n_rows = dmat->Info().num_row_;
size_t threshold_index = GradientBasedSampler::CalculateThresholdIndex(
@@ -298,28 +301,25 @@ GradientBasedSample ExternalMemoryGradientBasedSampling::Sample(common::Span<Gra
sample_row_index_.begin(),
ClearEmptyRows());
auto batch_iterator = dmat->GetBatches<EllpackPage>(batch_param_);
auto batch_iterator = dmat->GetBatches<EllpackPage>(ctx, batch_param_);
auto first_page = (*batch_iterator.begin()).Impl();
// Create a new ELLPACK page with empty rows.
page_.reset(); // Release the device memory first before reallocating
page_.reset(new EllpackPageImpl(batch_param_.gpu_id, first_page->Cuts(),
first_page->is_dense,
page_.reset(new EllpackPageImpl(ctx->gpu_id, first_page->Cuts(), first_page->is_dense,
first_page->row_stride, sample_rows));
// Compact the ELLPACK pages into the single sample page.
thrust::fill(dh::tbegin(page_->gidx_buffer), dh::tend(page_->gidx_buffer), 0);
for (auto& batch : batch_iterator) {
page_->Compact(batch_param_.gpu_id, batch.Impl(), dh::ToSpan(sample_row_index_));
page_->Compact(ctx->gpu_id, batch.Impl(), dh::ToSpan(sample_row_index_));
}
return {sample_rows, page_.get(), dh::ToSpan(gpair_)};
}
GradientBasedSampler::GradientBasedSampler(EllpackPageImpl const* page,
size_t n_rows,
const BatchParam& batch_param,
float subsample,
int sampling_method) {
GradientBasedSampler::GradientBasedSampler(Context const* ctx, EllpackPageImpl const* page,
size_t n_rows, const BatchParam& batch_param,
float subsample, int sampling_method) {
monitor_.Init("gradient_based_sampler");
bool is_sampling = subsample < 1.0;
@@ -346,7 +346,7 @@ GradientBasedSampler::GradientBasedSampler(EllpackPageImpl const* page,
}
} else {
if (is_external_memory) {
strategy_.reset(new ExternalMemoryNoSampling(page, n_rows, batch_param));
strategy_.reset(new ExternalMemoryNoSampling(ctx, page, n_rows, batch_param));
} else {
strategy_.reset(new NoSampling(page));
}
@@ -354,10 +354,10 @@ GradientBasedSampler::GradientBasedSampler(EllpackPageImpl const* page,
}
// Sample a DMatrix based on the given gradient pairs.
GradientBasedSample GradientBasedSampler::Sample(common::Span<GradientPair> gpair,
DMatrix* dmat) {
GradientBasedSample GradientBasedSampler::Sample(Context const* ctx,
common::Span<GradientPair> gpair, DMatrix* dmat) {
monitor_.Start("Sample");
GradientBasedSample sample = strategy_->Sample(gpair, dmat);
GradientBasedSample sample = strategy_->Sample(ctx, gpair, dmat);
monitor_.Stop("Sample");
return sample;
}

47
src/tree/gpu_hist/gradient_based_sampler.cuh
View File

@@ -24,7 +24,8 @@ struct GradientBasedSample {
class SamplingStrategy {
public:
/*! \brief Sample from a DMatrix based on the given gradient pairs. */
virtual GradientBasedSample Sample(common::Span<GradientPair> gpair, DMatrix* dmat) = 0;
virtual GradientBasedSample Sample(Context const* ctx, common::Span<GradientPair> gpair,
DMatrix* dmat) = 0;
virtual ~SamplingStrategy() = default;
};
@@ -32,7 +33,8 @@ class SamplingStrategy {
class NoSampling : public SamplingStrategy {
public:
explicit NoSampling(EllpackPageImpl const* page);
GradientBasedSample Sample(common::Span<GradientPair> gpair, DMatrix* dmat) override;
GradientBasedSample Sample(Context const* ctx, common::Span<GradientPair> gpair,
DMatrix* dmat) override;
private:
EllpackPageImpl const* page_;
@@ -41,10 +43,10 @@ class NoSampling : public SamplingStrategy {
/*! \brief No sampling in external memory mode. */
class ExternalMemoryNoSampling : public SamplingStrategy {
public:
ExternalMemoryNoSampling(EllpackPageImpl const* page,
size_t n_rows,
const BatchParam& batch_param);
GradientBasedSample Sample(common::Span<GradientPair> gpair, DMatrix* dmat) override;
ExternalMemoryNoSampling(Context const* ctx, EllpackPageImpl const* page, size_t n_rows,
BatchParam batch_param);
GradientBasedSample Sample(Context const* ctx, common::Span<GradientPair> gpair,
DMatrix* dmat) override;
private:
BatchParam batch_param_;
@@ -56,7 +58,8 @@ class ExternalMemoryNoSampling : public SamplingStrategy {
class UniformSampling : public SamplingStrategy {
public:
UniformSampling(EllpackPageImpl const* page, float subsample);
GradientBasedSample Sample(common::Span<GradientPair> gpair, DMatrix* dmat) override;
GradientBasedSample Sample(Context const* ctx, common::Span<GradientPair> gpair,
DMatrix* dmat) override;
private:
EllpackPageImpl const* page_;
@@ -66,10 +69,9 @@ class UniformSampling : public SamplingStrategy {
/*! \brief No sampling in external memory mode. */
class ExternalMemoryUniformSampling : public SamplingStrategy {
public:
ExternalMemoryUniformSampling(size_t n_rows,
BatchParam batch_param,
float subsample);
GradientBasedSample Sample(common::Span<GradientPair> gpair, DMatrix* dmat) override;
ExternalMemoryUniformSampling(size_t n_rows, BatchParam batch_param, float subsample);
GradientBasedSample Sample(Context const* ctx, common::Span<GradientPair> gpair,
DMatrix* dmat) override;
private:
BatchParam batch_param_;
@@ -82,11 +84,10 @@ class ExternalMemoryUniformSampling : public SamplingStrategy {
/*! \brief Gradient-based sampling in in-memory mode.. */
class GradientBasedSampling : public SamplingStrategy {
public:
GradientBasedSampling(EllpackPageImpl const* page,
size_t n_rows,
const BatchParam& batch_param,
GradientBasedSampling(EllpackPageImpl const* page, size_t n_rows, const BatchParam& batch_param,
float subsample);
GradientBasedSample Sample(common::Span<GradientPair> gpair, DMatrix* dmat) override;
GradientBasedSample Sample(Context const* ctx, common::Span<GradientPair> gpair,
DMatrix* dmat) override;
private:
EllpackPageImpl const* page_;
@@ -98,10 +99,9 @@ class GradientBasedSampling : public SamplingStrategy {
/*! \brief Gradient-based sampling in external memory mode.. */
class ExternalMemoryGradientBasedSampling : public SamplingStrategy {
public:
ExternalMemoryGradientBasedSampling(size_t n_rows,
BatchParam batch_param,
float subsample);
GradientBasedSample Sample(common::Span<GradientPair> gpair, DMatrix* dmat) override;
ExternalMemoryGradientBasedSampling(size_t n_rows, BatchParam batch_param, float subsample);
GradientBasedSample Sample(Context const* ctx, common::Span<GradientPair> gpair,
DMatrix* dmat) override;
private:
BatchParam batch_param_;
@@ -124,14 +124,11 @@ class ExternalMemoryGradientBasedSampling : public SamplingStrategy {
*/
class GradientBasedSampler {
public:
GradientBasedSampler(EllpackPageImpl const* page,
size_t n_rows,
const BatchParam& batch_param,
float subsample,
int sampling_method);
GradientBasedSampler(Context const* ctx, EllpackPageImpl const* page, size_t n_rows,
const BatchParam& batch_param, float subsample, int sampling_method);
/*! \brief Sample from a DMatrix based on the given gradient pairs. */
GradientBasedSample Sample(common::Span<GradientPair> gpair, DMatrix* dmat);
GradientBasedSample Sample(Context const* ctx, common::Span<GradientPair> gpair, DMatrix* dmat);
/*! \brief Calculate the threshold used to normalize sampling probabilities. */
static size_t CalculateThresholdIndex(common::Span<GradientPair> gpair,

9
src/tree/updater_approx.cc
View File

@@ -66,7 +66,7 @@ class GloablApproxBuilder {
partitioner_.clear();
// Generating the GHistIndexMatrix is quite slow, is there a way to speed it up?
for (auto const &page :
p_fmat->GetBatches<GHistIndexMatrix>(BatchSpec(*param_, hess, *task_))) {
p_fmat->GetBatches<GHistIndexMatrix>(ctx_, BatchSpec(*param_, hess, *task_))) {
if (n_total_bins == 0) {
n_total_bins = page.cut.TotalBins();
feature_values_ = page.cut;
@@ -97,7 +97,7 @@ class GloablApproxBuilder {
std::vector<CPUExpandEntry> nodes{best};
size_t i = 0;
auto space = ConstructHistSpace(partitioner_, nodes);
for (auto const &page : p_fmat->GetBatches<GHistIndexMatrix>(BatchSpec(*param_, hess))) {
for (auto const &page : p_fmat->GetBatches<GHistIndexMatrix>(ctx_, BatchSpec(*param_, hess))) {
histogram_builder_.BuildHist(i, space, page, p_tree, partitioner_.at(i).Partitions(), nodes,
{}, gpair);
i++;
@@ -148,7 +148,7 @@ class GloablApproxBuilder {
size_t i = 0;
auto space = ConstructHistSpace(partitioner_, nodes_to_build);
for (auto const &page : p_fmat->GetBatches<GHistIndexMatrix>(BatchSpec(*param_, hess))) {
for (auto const &page : p_fmat->GetBatches<GHistIndexMatrix>(ctx_, BatchSpec(*param_, hess))) {
histogram_builder_.BuildHist(i, space, page, p_tree, partitioner_.at(i).Partitions(),
nodes_to_build, nodes_to_sub, gpair);
i++;
@@ -214,7 +214,8 @@ class GloablApproxBuilder {
monitor_->Start("UpdatePosition");
size_t page_id = 0;
for (auto const &page : p_fmat->GetBatches<GHistIndexMatrix>(BatchSpec(*param_, hess))) {
for (auto const &page :
p_fmat->GetBatches<GHistIndexMatrix>(ctx_, BatchSpec(*param_, hess))) {
partitioner_.at(page_id).UpdatePosition(ctx_, page, applied, p_tree);
page_id++;
}

6
src/tree/updater_colmaker.cc
View File

@@ -76,7 +76,7 @@ class ColMaker: public TreeUpdater {
// Finds densities if we don't already have them
if (column_densities_.empty()) {
std::vector<size_t> column_size(dmat->Info().num_col_);
for (const auto &batch : dmat->GetBatches<SortedCSCPage>()) {
for (const auto &batch : dmat->GetBatches<SortedCSCPage>(ctx_)) {
auto page = batch.GetView();
for (auto i = 0u; i < batch.Size(); i++) {
column_size[i] += page[i].size();
@@ -467,7 +467,7 @@ class ColMaker: public TreeUpdater {
auto evaluator = tree_evaluator_.GetEvaluator();
auto feat_set = column_sampler_.GetFeatureSet(depth);
for (const auto &batch : p_fmat->GetBatches<SortedCSCPage>()) {
for (const auto &batch : p_fmat->GetBatches<SortedCSCPage>(ctx_)) {
this->UpdateSolution(batch, feat_set->HostVector(), gpair, p_fmat);
}
// after this each thread's stemp will get the best candidates, aggregate results
@@ -546,7 +546,7 @@ class ColMaker: public TreeUpdater {
}
std::sort(fsplits.begin(), fsplits.end());
fsplits.resize(std::unique(fsplits.begin(), fsplits.end()) - fsplits.begin());
for (const auto &batch : p_fmat->GetBatches<SortedCSCPage>()) {
for (const auto &batch : p_fmat->GetBatches<SortedCSCPage>(ctx_)) {
auto page = batch.GetView();
for (auto fid : fsplits) {
auto col = page[fid];

11
src/tree/updater_gpu_hist.cu
View File

@@ -218,7 +218,7 @@ struct GPUHistMakerDevice {
column_sampler(column_sampler_seed),
interaction_constraints(param, n_features),
batch_param(std::move(_batch_param)) {
sampler.reset(new GradientBasedSampler(page, _n_rows, batch_param, param.subsample,
sampler.reset(new GradientBasedSampler(ctx, page, _n_rows, batch_param, param.subsample,
param.sampling_method));
if (!param.monotone_constraints.empty()) {
// Copy assigning an empty vector causes an exception in MSVC debug builds
@@ -258,7 +258,7 @@ struct GPUHistMakerDevice {
dh::safe_cuda(cudaMemcpyAsync(
d_gpair.data().get(), dh_gpair->ConstDevicePointer(),
dh_gpair->Size() * sizeof(GradientPair), cudaMemcpyDeviceToDevice));
auto sample = sampler->Sample(dh::ToSpan(d_gpair), dmat);
auto sample = sampler->Sample(ctx_, dh::ToSpan(d_gpair), dmat);
page = sample.page;
gpair = sample.gpair;
@@ -808,11 +808,8 @@ class GPUHistMaker : public TreeUpdater {
uint32_t column_sampling_seed = common::GlobalRandom()();
collective::Broadcast(&column_sampling_seed, sizeof(column_sampling_seed), 0);
BatchParam batch_param{
ctx_->gpu_id,
param->max_bin,
};
auto page = (*dmat->GetBatches<EllpackPage>(batch_param).begin()).Impl();
auto batch_param = BatchParam{param->max_bin, TrainParam::DftSparseThreshold()};
auto page = (*dmat->GetBatches<EllpackPage>(ctx_, batch_param).begin()).Impl();
dh::safe_cuda(cudaSetDevice(ctx_->gpu_id));
info_->feature_types.SetDevice(ctx_->gpu_id);
maker.reset(new GPUHistMakerDevice<GradientSumT>(

26
src/tree/updater_quantile_hist.cc
View File

@@ -134,7 +134,7 @@ class MultiTargetHistBuilder {
std::vector<MultiExpandEntry> const &applied) {
monitor_->Start(__func__);
std::size_t page_id{0};
for (auto const &page : p_fmat->GetBatches<GHistIndexMatrix>(HistBatch(this->param_))) {
for (auto const &page : p_fmat->GetBatches<GHistIndexMatrix>(ctx_, HistBatch(this->param_))) {
this->partitioner_.at(page_id).UpdatePosition(this->ctx_, page, applied, p_tree);
page_id++;
}
@@ -152,7 +152,7 @@ class MultiTargetHistBuilder {
std::size_t page_id = 0;
bst_bin_t n_total_bins = 0;
partitioner_.clear();
for (auto const &page : p_fmat->GetBatches<GHistIndexMatrix>(HistBatch(param_))) {
for (auto const &page : p_fmat->GetBatches<GHistIndexMatrix>(ctx_, HistBatch(param_))) {
if (n_total_bins == 0) {
n_total_bins = page.cut.TotalBins();
} else {
@@ -206,7 +206,7 @@ class MultiTargetHistBuilder {
std::vector<MultiExpandEntry> nodes{best};
std::size_t i = 0;
auto space = ConstructHistSpace(partitioner_, nodes);
for (auto const &page : p_fmat->GetBatches<GHistIndexMatrix>(HistBatch(param_))) {
for (auto const &page : p_fmat->GetBatches<GHistIndexMatrix>(ctx_, HistBatch(param_))) {
for (bst_target_t t{0}; t < n_targets; ++t) {
auto t_gpair = gpair.Slice(linalg::All(), t);
histogram_builder_[t].BuildHist(i, space, page, p_tree, partitioner_.at(i).Partitions(),
@@ -225,7 +225,7 @@ class MultiTargetHistBuilder {
for (bst_target_t t{0}; t < p_tree->NumTargets(); ++t) {
hists.push_back(&histogram_builder_[t].Histogram());
}
for (auto const &gmat : p_fmat->GetBatches<GHistIndexMatrix>(HistBatch(param_))) {
for (auto const &gmat : p_fmat->GetBatches<GHistIndexMatrix>(ctx_, HistBatch(param_))) {
evaluator_->EvaluateSplits(*p_tree, hists, gmat.cut, &nodes);
break;
}
@@ -263,7 +263,7 @@ class MultiTargetHistBuilder {
std::size_t i = 0;
auto space = ConstructHistSpace(partitioner_, nodes_to_build);
for (auto const &page : p_fmat->GetBatches<GHistIndexMatrix>(HistBatch(param_))) {
for (auto const &page : p_fmat->GetBatches<GHistIndexMatrix>(ctx_, HistBatch(param_))) {
for (std::size_t t = 0; t < p_tree->NumTargets(); ++t) {
auto t_gpair = gpair.Slice(linalg::All(), t);
// Make sure the gradient matrix is f-order.
@@ -283,7 +283,7 @@ class MultiTargetHistBuilder {
for (bst_target_t t{0}; t < p_tree->NumTargets(); ++t) {
hists.push_back(&histogram_builder_[t].Histogram());
}
for (auto const &gmat : p_fmat->GetBatches<GHistIndexMatrix>(HistBatch(param_))) {
for (auto const &gmat : p_fmat->GetBatches<GHistIndexMatrix>(ctx_, HistBatch(param_))) {
evaluator_->EvaluateSplits(*p_tree, hists, gmat.cut, best_splits);
break;
}
@@ -383,7 +383,7 @@ class HistBuilder {
std::size_t page_id{0};
bst_bin_t n_total_bins{0};
partitioner_.clear();
for (auto const &page : fmat->GetBatches<GHistIndexMatrix>(HistBatch(param_))) {
for (auto const &page : fmat->GetBatches<GHistIndexMatrix>(ctx_, HistBatch(param_))) {
if (n_total_bins == 0) {
n_total_bins = page.cut.TotalBins();
} else {
@@ -406,7 +406,7 @@ class HistBuilder {
monitor_->Start(__func__);
auto const &histograms = histogram_builder_->Histogram();
auto ft = p_fmat->Info().feature_types.ConstHostSpan();
for (auto const &gmat : p_fmat->GetBatches<GHistIndexMatrix>(HistBatch(param_))) {
for (auto const &gmat : p_fmat->GetBatches<GHistIndexMatrix>(ctx_, HistBatch(param_))) {
evaluator_->EvaluateSplits(histograms, gmat.cut, ft, *p_tree, best_splits);
break;
}
@@ -423,7 +423,7 @@ class HistBuilder {
std::size_t page_id = 0;
auto space = ConstructHistSpace(partitioner_, {node});
for (auto const &gidx : p_fmat->GetBatches<GHistIndexMatrix>(HistBatch(param_))) {
for (auto const &gidx : p_fmat->GetBatches<GHistIndexMatrix>(ctx_, HistBatch(param_))) {
std::vector<CPUExpandEntry> nodes_to_build{node};
std::vector<CPUExpandEntry> nodes_to_sub;
this->histogram_builder_->BuildHist(page_id, space, gidx, p_tree,
@@ -439,7 +439,7 @@ class HistBuilder {
* Specialized code for dense data: For dense data (with no missing value), the sum
* of gradient histogram is equal to snode[nid]
*/
auto const &gmat = *(p_fmat->GetBatches<GHistIndexMatrix>(HistBatch(param_)).begin());
auto const &gmat = *(p_fmat->GetBatches<GHistIndexMatrix>(ctx_, HistBatch(param_)).begin());
std::vector<std::uint32_t> const &row_ptr = gmat.cut.Ptrs();
CHECK_GE(row_ptr.size(), 2);
std::uint32_t const ibegin = row_ptr[0];
@@ -467,7 +467,7 @@ class HistBuilder {
std::vector<CPUExpandEntry> entries{node};
monitor_->Start("EvaluateSplits");
auto ft = p_fmat->Info().feature_types.ConstHostSpan();
for (auto const &gmat : p_fmat->GetBatches<GHistIndexMatrix>(HistBatch(param_))) {
for (auto const &gmat : p_fmat->GetBatches<GHistIndexMatrix>(ctx_, HistBatch(param_))) {
evaluator_->EvaluateSplits(histogram_builder_->Histogram(), gmat.cut, ft, *p_tree,
&entries);
break;
@@ -503,7 +503,7 @@ class HistBuilder {
std::size_t page_id{0};
auto space = ConstructHistSpace(partitioner_, nodes_to_build);
for (auto const &gidx : p_fmat->GetBatches<GHistIndexMatrix>(HistBatch(param_))) {
for (auto const &gidx : p_fmat->GetBatches<GHistIndexMatrix>(ctx_, HistBatch(param_))) {
histogram_builder_->BuildHist(page_id, space, gidx, p_tree,
partitioner_.at(page_id).Partitions(), nodes_to_build,
nodes_to_sub, gpair.Values());
@@ -515,7 +515,7 @@ class HistBuilder {
std::vector<CPUExpandEntry> const &applied) {
monitor_->Start(__func__);
std::size_t page_id{0};
for (auto const &page : p_fmat->GetBatches<GHistIndexMatrix>(HistBatch(this->param_))) {
for (auto const &page : p_fmat->GetBatches<GHistIndexMatrix>(ctx_, HistBatch(param_))) {
this->partitioner_.at(page_id).UpdatePosition(this->ctx_, page, applied, p_tree);
page_id++;
}