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[CI] Test building for 32-bit arch (#10021)

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* [CI] Test building for 32-bit arch

* Update CMakeLists.txt

* Fix yaml

* Use Debian container

* Remove -Werror for 32-bit

* Revert "Remove -Werror for 32-bit"

This reverts commit c652bc6a037361bcceaf56fb01863210b462793d.

* Don't error for overloaded-virtual warning

* Ignore some warnings from dmlc-core

* Fix compiler warnings

* Fix formatting

* Apply suggestions from code review

Co-authored-by: Jiaming Yuan <jm.yuan@outlook.com>

* Add more cast

---------

Co-authored-by: Jiaming Yuan <jm.yuan@outlook.com>
This commit is contained in:
Philip Hyunsu Cho
2024-01-31 13:20:51 -08:00
committed by GitHub
parent 234674a0a6
commit 4dfbe2a893
14 changed files with 84 additions and 35 deletions
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6
src/c_api/c_api.cc
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@@ -1,5 +1,5 @@
/**
* Copyright 2014-2023 by XGBoost Contributors
* Copyright 2014-2024 by XGBoost Contributors
*/
#include "xgboost/c_api.h"
@@ -991,8 +991,8 @@ XGB_DLL int XGBoosterBoostOneIter(BoosterHandle handle, DMatrixHandle dtrain, bs
auto *learner = static_cast<Learner *>(handle);
auto ctx = learner->Ctx()->MakeCPU();
auto t_grad = linalg::MakeTensorView(&ctx, common::Span{grad, len}, len);
auto t_hess = linalg::MakeTensorView(&ctx, common::Span{hess, len}, len);
auto t_grad = linalg::MakeTensorView(&ctx, common::Span{grad, static_cast<size_t>(len)}, len);
auto t_hess = linalg::MakeTensorView(&ctx, common::Span{hess, static_cast<size_t>(len)}, len);
auto s_grad = linalg::ArrayInterfaceStr(t_grad);
auto s_hess = linalg::ArrayInterfaceStr(t_hess);

10
src/common/column_matrix.h
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@@ -1,5 +1,5 @@
/**
* Copyright 2017-2023, XGBoost Contributors
* Copyright 2017-2024, XGBoost Contributors
* \file column_matrix.h
* \brief Utility for fast column-wise access
* \author Philip Cho
@@ -176,7 +176,7 @@ class ColumnMatrix {
void SetValid(typename LBitField32::index_type i) { missing.Clear(i); }
/** @brief assign the storage to the view. */
void InitView() {
missing = LBitField32{Span{storage.data(), storage.size()}};
missing = LBitField32{Span{storage.data(), static_cast<size_t>(storage.size())}};
}
void GrowTo(std::size_t n_elements, bool init) {
@@ -318,8 +318,8 @@ class ColumnMatrix {
common::Span<const BinIdxType> bin_index = {
reinterpret_cast<const BinIdxType*>(&index_[feature_offset * bins_type_size_]),
column_size};
return std::move(DenseColumnIter<BinIdxType, any_missing>{
bin_index, static_cast<bst_bin_t>(index_base_[fidx]), missing_.missing, feature_offset});
return DenseColumnIter<BinIdxType, any_missing>{
bin_index, static_cast<bst_bin_t>(index_base_[fidx]), missing_.missing, feature_offset};
}
// all columns are dense column and has no missing value
@@ -332,7 +332,7 @@ class ColumnMatrix {
DispatchBinType(bins_type_size_, [&](auto t) {
using ColumnBinT = decltype(t);
auto column_index = Span<ColumnBinT>{reinterpret_cast<ColumnBinT*>(index_.data()),
index_.size() / sizeof(ColumnBinT)};
static_cast<size_t>(index_.size() / sizeof(ColumnBinT))};
ParallelFor(n_samples, n_threads, [&](auto rid) {
rid += base_rowid;
const size_t ibegin = rid * n_features;

10
src/common/hist_util.h
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@@ -1,5 +1,5 @@
/**
* Copyright 2017-2023 by XGBoost Contributors
* Copyright 2017-2024 by XGBoost Contributors
* \file hist_util.h
* \brief Utility for fast histogram aggregation
* \author Philip Cho, Tianqi Chen
@@ -113,8 +113,8 @@ class HistogramCuts {
auto end = ptrs[column_id + 1];
auto beg = ptrs[column_id];
auto it = std::upper_bound(values.cbegin() + beg, values.cbegin() + end, value);
auto idx = it - values.cbegin();
idx -= !!(idx == end);
auto idx = static_cast<bst_bin_t>(it - values.cbegin());
idx -= !!(idx == static_cast<bst_bin_t>(end));
return idx;
}
@@ -136,8 +136,8 @@ class HistogramCuts {
auto beg = ptrs[fidx] + vals.cbegin();
// Truncates the value in case it's not perfectly rounded.
auto v = static_cast<float>(common::AsCat(value));
auto bin_idx = std::lower_bound(beg, end, v) - vals.cbegin();
if (bin_idx == ptrs.at(fidx + 1)) {
auto bin_idx = static_cast<bst_bin_t>(std::lower_bound(beg, end, v) - vals.cbegin());
if (bin_idx == static_cast<bst_bin_t>(ptrs.at(fidx + 1))) {
bin_idx -= 1;
}
return bin_idx;

4
src/common/ref_resource_view.h
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@@ -1,5 +1,5 @@
/**
* Copyright 2023, XGBoost Contributors
* Copyright 2023-2024, XGBoost Contributors
*/
#ifndef XGBOOST_COMMON_REF_RESOURCE_VIEW_H_
#define XGBOOST_COMMON_REF_RESOURCE_VIEW_H_
@@ -76,7 +76,7 @@ class RefResourceView {
[[nodiscard]] size_type size() const { return size_; } // NOLINT
[[nodiscard]] size_type size_bytes() const { // NOLINT
return Span<const value_type>{data(), size()}.size_bytes();
return Span<const value_type>{data(), static_cast<size_t>(size())}.size_bytes();
}
[[nodiscard]] value_type* data() { return ptr_; }; // NOLINT
[[nodiscard]] value_type const* data() const { return ptr_; }; // NOLINT

5
src/data/gradient_index.cc
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@@ -1,5 +1,5 @@
/**
* Copyright 2017-2023, XGBoost Contributors
* Copyright 2017-2024, XGBoost Contributors
* \brief Data type for fast histogram aggregation.
*/
#include "gradient_index.h"
@@ -148,7 +148,8 @@ void GHistIndexMatrix::ResizeIndex(const size_t n_index, const bool isDense) {
new_vec = {new_ptr, n_bytes / sizeof(std::uint8_t), malloc_resource};
}
this->data = std::move(new_vec);
this->index = common::Index{common::Span{data.data(), data.size()}, t_size};
this->index = common::Index{common::Span{data.data(), static_cast<size_t>(data.size())},
t_size};
};
if ((MaxNumBinPerFeat() - 1 <= static_cast<int>(std::numeric_limits<uint8_t>::max())) &&

6
src/data/gradient_index_format.cc
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@@ -1,5 +1,5 @@
/**
* Copyright 2021-2023 XGBoost contributors
* Copyright 2021-2024 XGBoost contributors
*/
#include <cstddef> // for size_t
#include <cstdint> // for uint8_t
@@ -40,7 +40,9 @@ class GHistIndexRawFormat : public SparsePageFormat<GHistIndexMatrix> {
return false;
}
// - index
page->index = common::Index{common::Span{page->data.data(), page->data.size()}, size_type};
page->index =
common::Index{common::Span{page->data.data(), static_cast<size_t>(page->data.size())},
size_type};
// hit count
if (!common::ReadVec(fi, &page->hit_count)) {

4
src/predictor/predictor.cc
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@@ -1,5 +1,5 @@
/**
* Copyright 2017-2023 by Contributors
* Copyright 2017-2024 by Contributors
*/
#include "xgboost/predictor.h"
@@ -46,7 +46,7 @@ void ValidateBaseMarginShape(linalg::Tensor<float, D> const& margin, bst_row_t n
void Predictor::InitOutPredictions(const MetaInfo& info, HostDeviceVector<bst_float>* out_preds,
const gbm::GBTreeModel& model) const {
CHECK_NE(model.learner_model_param->num_output_group, 0);
std::size_t n{model.learner_model_param->OutputLength() * info.num_row_};
auto n = static_cast<size_t>(model.learner_model_param->OutputLength() * info.num_row_);
const HostDeviceVector<bst_float>* base_margin = info.base_margin_.Data();
if (ctx_->Device().IsCUDA()) {

8
src/tree/hist/hist_cache.h
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@@ -1,5 +1,5 @@
/**
* Copyright 2023 by XGBoost Contributors
* Copyright 2023-2024 by XGBoost Contributors
*/
#ifndef XGBOOST_TREE_HIST_HIST_CACHE_H_
#define XGBOOST_TREE_HIST_HIST_CACHE_H_
@@ -48,11 +48,13 @@ class BoundedHistCollection {
BoundedHistCollection() = default;
common::GHistRow operator[](std::size_t idx) {
auto offset = node_map_.at(idx);
return common::Span{data_->data(), data_->size()}.subspan(offset, n_total_bins_);
return common::Span{data_->data(), static_cast<size_t>(data_->size())}.subspan(
offset, n_total_bins_);
}
common::ConstGHistRow operator[](std::size_t idx) const {
auto offset = node_map_.at(idx);
return common::Span{data_->data(), data_->size()}.subspan(offset, n_total_bins_);
return common::Span{data_->data(), static_cast<size_t>(data_->size())}.subspan(
offset, n_total_bins_);
}
void Reset(bst_bin_t n_total_bins, std::size_t n_cached_nodes) {
n_total_bins_ = n_total_bins;