Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Deterministic result for element-wise/mclass metrics. #7303

Merged
merged 3 commits into from
Oct 13, 2021
+140 −46
Merged

Deterministic result for element-wise/mclass metrics. #7303

Changes from 1 commit
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
df958c8
Deterministic result for element-wise metrics.
trivialfis Oct 10, 2021
e8765df
Add deterministic checks.
trivialfis Oct 12, 2021
ef0af04
Work on multi-class metrics.
trivialfis Oct 12, 2021
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
50 changes: 24 additions & 26 deletions src/metric/elementwise_metric.cu
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -14,6 +14,7 @@
#include "metric_common.h"
#include "../common/math.h"
#include "../common/common.h"
#include "../common/threading_utils.h"

#if defined(XGBOOST_USE_CUDA)
#include <thrust/execution_policy.h> // thrust::cuda::par
Expand All @@ -34,29 +35,29 @@ class ElementWiseMetricsReduction {
public:
explicit ElementWiseMetricsReduction(EvalRow policy) : policy_(std::move(policy)) {}

PackedReduceResult CpuReduceMetrics(
const HostDeviceVector<bst_float>& weights,
const HostDeviceVector<bst_float>& labels,
const HostDeviceVector<bst_float>& preds) const {
PackedReduceResult
CpuReduceMetrics(const HostDeviceVector<bst_float> &weights,
const HostDeviceVector<bst_float> &labels,
const HostDeviceVector<bst_float> &preds,
int32_t n_threads) const {
size_t ndata = labels.Size();

const auto& h_labels = labels.HostVector();
const auto& h_weights = weights.HostVector();
const auto& h_preds = preds.HostVector();

bst_float residue_sum = 0;
bst_float weights_sum = 0;

dmlc::OMPException exc;
#pragma omp parallel for reduction(+: residue_sum, weights_sum) schedule(static)
for (omp_ulong i = 0; i < ndata; ++i) {
exc.Run([&]() {
const bst_float wt = h_weights.size() > 0 ? h_weights[i] : 1.0f;
residue_sum += policy_.EvalRow(h_labels[i], h_preds[i]) * wt;
weights_sum += wt;
});
}
exc.Rethrow();
std::vector<double> score_tloc(n_threads, 0.0);
std::vector<double> weight_tloc(n_threads, 0.0);

common::ParallelFor(ndata, n_threads, [&](size_t i) {
float wt = h_weights.size() > 0 ? h_weights[i] : 1.0f;
auto t_idx = omp_get_thread_num();
score_tloc[t_idx] += policy_.EvalRow(h_labels[i], h_preds[i]) * wt;
weight_tloc[t_idx] += wt;
});
double residue_sum = std::accumulate(score_tloc.cbegin(), score_tloc.cend(), 0.0);
double weights_sum = std::accumulate(weight_tloc.cbegin(), weight_tloc.cend(), 0.0);

PackedReduceResult res { residue_sum, weights_sum };
return res;
}
Expand Down Expand Up @@ -100,19 +101,19 @@ class ElementWiseMetricsReduction {
#endif // XGBOOST_USE_CUDA

PackedReduceResult Reduce(
const GenericParameter &tparam,
int device,
const GenericParameter &ctx,
const HostDeviceVector<bst_float>& weights,
const HostDeviceVector<bst_float>& labels,
const HostDeviceVector<bst_float>& preds) {
PackedReduceResult result;

if (device < 0) {
result = CpuReduceMetrics(weights, labels, preds);
if (ctx.gpu_id < 0) {
auto n_threads = ctx.Threads();
result = CpuReduceMetrics(weights, labels, preds, n_threads);
}
#if defined(XGBOOST_USE_CUDA)
else { // NOLINT
device_ = device;
device_ = ctx.gpu_id;
preds.SetDevice(device_);
labels.SetDevice(device_);
weights.SetDevice(device_);
Expand Down Expand Up @@ -365,10 +366,7 @@ struct EvalEWiseBase : public Metric {
CHECK_EQ(preds.Size(), info.labels_.Size())
<< "label and prediction size not match, "
<< "hint: use merror or mlogloss for multi-class classification";
int device = tparam_->gpu_id;

auto result =
reducer_.Reduce(*tparam_, device, info.weights_, info.labels_, preds);
auto result = reducer_.Reduce(*tparam_, info.weights_, info.labels_, preds);

double dat[2] { result.Residue(), result.Weights() };

Expand Down