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Replaced std::vector with HostDeviceVector in MetaInfo and SparsePage. (#3446)

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* Replaced std::vector with HostDeviceVector in MetaInfo and SparsePage.

- added distributions to HostDeviceVector
- using HostDeviceVector for labels, weights and base margings in MetaInfo
- using HostDeviceVector for offset and data in SparsePage
- other necessary refactoring

* Added const version of HostDeviceVector API calls.

- const versions added to calls that can trigger data transfers, e.g. DevicePointer()
- updated the code that uses HostDeviceVector
- objective functions now accept const HostDeviceVector<bst_float>& for predictions

* Updated src/linear/updater_gpu_coordinate.cu.

* Added read-only state for HostDeviceVector sync.

- this means no copies are performed if both host and devices access
  the HostDeviceVector read-only

* Fixed linter and test errors.

- updated the lz4 plugin
- added ConstDeviceSpan to HostDeviceVector
- using device % dh::NVisibleDevices() for the physical device number,
  e.g. in calls to cudaSetDevice()

* Fixed explicit template instantiation errors for HostDeviceVector.

- replaced HostDeviceVector<unsigned int> with HostDeviceVector<int>

* Fixed HostDeviceVector tests that require multiple GPUs.

- added a mock set device handler; when set, it is called instead of cudaSetDevice()
This commit is contained in:
Andy Adinets
2018-08-30 04:28:47 +02:00
committed by Rory Mitchell
parent 58d783df16
commit 72cd1517d6
45 changed files with 1141 additions and 560 deletions
Download Patch File Download Diff File Expand all files Collapse all files

12
src/metric/elementwise_metric.cc
View File

@@ -24,16 +24,18 @@ struct EvalEWiseBase : public Metric {
bst_float Eval(const std::vector<bst_float>& preds,
const MetaInfo& info,
bool distributed) const override {
CHECK_NE(info.labels_.size(), 0U) << "label set cannot be empty";
CHECK_EQ(preds.size(), info.labels_.size())
CHECK_NE(info.labels_.Size(), 0U) << "label set cannot be empty";
CHECK_EQ(preds.size(), info.labels_.Size())
<< "label and prediction size not match, "
<< "hint: use merror or mlogloss for multi-class classification";
const auto ndata = static_cast<omp_ulong>(info.labels_.size());
const auto ndata = static_cast<omp_ulong>(info.labels_.Size());
double sum = 0.0, wsum = 0.0;
const auto& labels = info.labels_.HostVector();
const auto& weights = info.weights_.HostVector();
#pragma omp parallel for reduction(+: sum, wsum) schedule(static)
for (omp_ulong i = 0; i < ndata; ++i) {
const bst_float wt = info.GetWeight(i);
sum += static_cast<const Derived*>(this)->EvalRow(info.labels_[i], preds[i]) * wt;
const bst_float wt = weights.size() > 0 ? weights[i] : 1.0f;
sum += static_cast<const Derived*>(this)->EvalRow(labels[i], preds[i]) * wt;
wsum += wt;
}
double dat[2]; dat[0] = sum, dat[1] = wsum;

16
src/metric/multiclass_metric.cc
View File

@@ -23,20 +23,24 @@ struct EvalMClassBase : public Metric {
bst_float Eval(const std::vector<bst_float> &preds,
const MetaInfo &info,
bool distributed) const override {
CHECK_NE(info.labels_.size(), 0U) << "label set cannot be empty";
CHECK(preds.size() % info.labels_.size() == 0)
CHECK_NE(info.labels_.Size(), 0U) << "label set cannot be empty";
CHECK(preds.size() % info.labels_.Size() == 0)
<< "label and prediction size not match";
const size_t nclass = preds.size() / info.labels_.size();
const size_t nclass = preds.size() / info.labels_.Size();
CHECK_GE(nclass, 1U)
<< "mlogloss and merror are only used for multi-class classification,"
<< " use logloss for binary classification";
const auto ndata = static_cast<bst_omp_uint>(info.labels_.size());
const auto ndata = static_cast<bst_omp_uint>(info.labels_.Size());
double sum = 0.0, wsum = 0.0;
int label_error = 0;
const auto& labels = info.labels_.HostVector();
const auto& weights = info.weights_.HostVector();
#pragma omp parallel for reduction(+: sum, wsum) schedule(static)
for (bst_omp_uint i = 0; i < ndata; ++i) {
const bst_float wt = info.GetWeight(i);
auto label = static_cast<int>(info.labels_[i]);
const bst_float wt = weights.size() > 0 ? weights[i] : 1.0f;
auto label = static_cast<int>(labels[i]);
if (label >= 0 && label < static_cast<int>(nclass)) {
sum += Derived::EvalRow(label,
preds.data() + i * nclass,

45
src/metric/rank_metric.cc
View File

@@ -32,7 +32,7 @@ struct EvalAMS : public Metric {
CHECK(!distributed) << "metric AMS do not support distributed evaluation";
using namespace std; // NOLINT(*)
const auto ndata = static_cast<bst_omp_uint>(info.labels_.size());
const auto ndata = static_cast<bst_omp_uint>(info.labels_.Size());
std::vector<std::pair<bst_float, unsigned> > rec(ndata);
#pragma omp parallel for schedule(static)
@@ -45,10 +45,11 @@ struct EvalAMS : public Metric {
const double br = 10.0;
unsigned thresindex = 0;
double s_tp = 0.0, b_fp = 0.0, tams = 0.0;
const auto& labels = info.labels_.HostVector();
for (unsigned i = 0; i < static_cast<unsigned>(ndata-1) && i < ntop; ++i) {
const unsigned ridx = rec[i].second;
const bst_float wt = info.GetWeight(ridx);
if (info.labels_[ridx] > 0.5f) {
if (labels[ridx] > 0.5f) {
s_tp += wt;
} else {
b_fp += wt;
@@ -84,14 +85,14 @@ struct EvalAuc : public Metric {
bst_float Eval(const std::vector<bst_float> &preds,
const MetaInfo &info,
bool distributed) const override {
CHECK_NE(info.labels_.size(), 0U) << "label set cannot be empty";
CHECK_EQ(preds.size(), info.labels_.size())
CHECK_NE(info.labels_.Size(), 0U) << "label set cannot be empty";
CHECK_EQ(preds.size(), info.labels_.Size())
<< "label size predict size not match";
std::vector<unsigned> tgptr(2, 0);
tgptr[1] = static_cast<unsigned>(info.labels_.size());
tgptr[1] = static_cast<unsigned>(info.labels_.Size());
const std::vector<unsigned> &gptr = info.group_ptr_.size() == 0 ? tgptr : info.group_ptr_;
CHECK_EQ(gptr.back(), info.labels_.size())
CHECK_EQ(gptr.back(), info.labels_.Size())
<< "EvalAuc: group structure must match number of prediction";
const auto ngroup = static_cast<bst_omp_uint>(gptr.size() - 1);
// sum statistics
@@ -99,6 +100,7 @@ struct EvalAuc : public Metric {
int auc_error = 0;
// each thread takes a local rec
std::vector< std::pair<bst_float, unsigned> > rec;
const auto& labels = info.labels_.HostVector();
for (bst_omp_uint k = 0; k < ngroup; ++k) {
rec.clear();
for (unsigned j = gptr[k]; j < gptr[k + 1]; ++j) {
@@ -110,7 +112,7 @@ struct EvalAuc : public Metric {
double sum_npos = 0.0, sum_nneg = 0.0, buf_pos = 0.0, buf_neg = 0.0;
for (size_t j = 0; j < rec.size(); ++j) {
const bst_float wt = info.GetWeight(rec[j].second);
const bst_float ctr = info.labels_[rec[j].second];
const bst_float ctr = labels[rec[j].second];
// keep bucketing predictions in same bucket
if (j != 0 && rec[j].first != rec[j - 1].first) {
sum_pospair += buf_neg * (sum_npos + buf_pos *0.5);
@@ -156,7 +158,7 @@ struct EvalRankList : public Metric {
bst_float Eval(const std::vector<bst_float> &preds,
const MetaInfo &info,
bool distributed) const override {
CHECK_EQ(preds.size(), info.labels_.size())
CHECK_EQ(preds.size(), info.labels_.Size())
<< "label size predict size not match";
// quick consistency when group is not available
std::vector<unsigned> tgptr(2, 0);
@@ -168,6 +170,7 @@ struct EvalRankList : public Metric {
const auto ngroup = static_cast<bst_omp_uint>(gptr.size() - 1);
// sum statistics
double sum_metric = 0.0f;
const auto& labels = info.labels_.HostVector();
#pragma omp parallel reduction(+:sum_metric)
{
// each thread takes a local rec
@@ -176,7 +179,7 @@ struct EvalRankList : public Metric {
for (bst_omp_uint k = 0; k < ngroup; ++k) {
rec.clear();
for (unsigned j = gptr[k]; j < gptr[k + 1]; ++j) {
rec.emplace_back(preds[j], static_cast<int>(info.labels_[j]));
rec.emplace_back(preds[j], static_cast<int>(labels[j]));
}
sum_metric += this->EvalMetric(rec);
}
@@ -314,7 +317,7 @@ struct EvalCox : public Metric {
CHECK(!distributed) << "Cox metric does not support distributed evaluation";
using namespace std; // NOLINT(*)
const auto ndata = static_cast<bst_omp_uint>(info.labels_.size());
const auto ndata = static_cast<bst_omp_uint>(info.labels_.Size());
const std::vector<size_t> &label_order = info.LabelAbsSort();
// pre-compute a sum for the denominator
@@ -326,9 +329,10 @@ struct EvalCox : public Metric {
double out = 0;
double accumulated_sum = 0;
bst_omp_uint num_events = 0;
const auto& labels = info.labels_.HostVector();
for (bst_omp_uint i = 0; i < ndata; ++i) {
const size_t ind = label_order[i];
const auto label = info.labels_[ind];
const auto label = labels[ind];
if (label > 0) {
out -= log(preds[ind]) - log(exp_p_sum);
++num_events;
@@ -336,7 +340,7 @@ struct EvalCox : public Metric {
// only update the denominator after we move forward in time (labels are sorted)
accumulated_sum += preds[ind];
if (i == ndata - 1 || std::abs(label) < std::abs(info.labels_[label_order[i + 1]])) {
if (i == ndata - 1 || std::abs(label) < std::abs(labels[label_order[i + 1]])) {
exp_p_sum -= accumulated_sum;
accumulated_sum = 0;
}
@@ -358,14 +362,14 @@ struct EvalAucPR : public Metric {
bst_float Eval(const std::vector<bst_float> &preds, const MetaInfo &info,
bool distributed) const override {
CHECK_NE(info.labels_.size(), 0U) << "label set cannot be empty";
CHECK_EQ(preds.size(), info.labels_.size())
CHECK_NE(info.labels_.Size(), 0U) << "label set cannot be empty";
CHECK_EQ(preds.size(), info.labels_.Size())
<< "label size predict size not match";
std::vector<unsigned> tgptr(2, 0);
tgptr[1] = static_cast<unsigned>(info.labels_.size());
tgptr[1] = static_cast<unsigned>(info.labels_.Size());
const std::vector<unsigned> &gptr =
info.group_ptr_.size() == 0 ? tgptr : info.group_ptr_;
CHECK_EQ(gptr.back(), info.labels_.size())
CHECK_EQ(gptr.back(), info.labels_.Size())
<< "EvalAucPR: group structure must match number of prediction";
const auto ngroup = static_cast<bst_omp_uint>(gptr.size() - 1);
// sum statistics
@@ -373,13 +377,14 @@ struct EvalAucPR : public Metric {
int auc_error = 0, auc_gt_one = 0;
// each thread takes a local rec
std::vector<std::pair<bst_float, unsigned>> rec;
const auto& labels = info.labels_.HostVector();
for (bst_omp_uint k = 0; k < ngroup; ++k) {
double total_pos = 0.0;
double total_neg = 0.0;
rec.clear();
for (unsigned j = gptr[k]; j < gptr[k + 1]; ++j) {
total_pos += info.GetWeight(j) * info.labels_[j];
total_neg += info.GetWeight(j) * (1.0f - info.labels_[j]);
total_pos += info.GetWeight(j) * labels[j];
total_neg += info.GetWeight(j) * (1.0f - labels[j]);
rec.emplace_back(preds[j], j);
}
XGBOOST_PARALLEL_SORT(rec.begin(), rec.end(), common::CmpFirst);
@@ -390,8 +395,8 @@ struct EvalAucPR : public Metric {
// calculate AUC
double tp = 0.0, prevtp = 0.0, fp = 0.0, prevfp = 0.0, h = 0.0, a = 0.0, b = 0.0;
for (size_t j = 0; j < rec.size(); ++j) {
tp += info.GetWeight(rec[j].second) * info.labels_[rec[j].second];
fp += info.GetWeight(rec[j].second) * (1.0f - info.labels_[rec[j].second]);
tp += info.GetWeight(rec[j].second) * labels[rec[j].second];
fp += info.GetWeight(rec[j].second) * (1.0f - labels[rec[j].second]);
if ((j < rec.size() - 1 && rec[j].first != rec[j + 1].first) || j == rec.size() - 1) {
if (tp == prevtp) {
a = 1.0;