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Avoid omp reduction in coordinate descent and aft metrics. (#7316)

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Aside from the omp issue, parameter configuration for aft metric is simplified.
This commit is contained in:
Jiaming Yuan
2021-10-17 15:55:49 +08:00
committed by GitHub
parent f56e2e9a66
commit fb1a9e6bc5
4 changed files with 112 additions and 70 deletions
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102
src/metric/survival_metric.cu
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@@ -18,6 +18,7 @@
#include "metric_common.h"
#include "../common/math.h"
#include "../common/survival_util.h"
#include "../common/threading_utils.h"
#if defined(XGBOOST_USE_CUDA)
#include <thrust/execution_policy.h> // thrust::cuda::par
@@ -42,11 +43,12 @@ class ElementWiseSurvivalMetricsReduction {
policy_ = policy;
}
PackedReduceResult CpuReduceMetrics(
const HostDeviceVector<bst_float>& weights,
const HostDeviceVector<bst_float>& labels_lower_bound,
const HostDeviceVector<bst_float>& labels_upper_bound,
const HostDeviceVector<bst_float>& preds) const {
PackedReduceResult
CpuReduceMetrics(const HostDeviceVector<bst_float> &weights,
const HostDeviceVector<bst_float> &labels_lower_bound,
const HostDeviceVector<bst_float> &labels_upper_bound,
const HostDeviceVector<bst_float> &preds,
int32_t n_threads) const {
size_t ndata = labels_lower_bound.Size();
CHECK_EQ(ndata, labels_upper_bound.Size());
@@ -55,22 +57,24 @@ class ElementWiseSurvivalMetricsReduction {
const auto& h_weights = weights.HostVector();
const auto& h_preds = preds.HostVector();
double residue_sum = 0;
double weights_sum = 0;
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) {
const double wt =
h_weights.empty() ? 1.0 : static_cast<double>(h_weights[i]);
auto t_idx = omp_get_thread_num();
score_tloc[t_idx] +=
policy_.EvalRow(static_cast<double>(h_labels_lower_bound[i]),
static_cast<double>(h_labels_upper_bound[i]),
static_cast<double>(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);
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 double wt = h_weights.empty() ? 1.0 : static_cast<double>(h_weights[i]);
residue_sum += policy_.EvalRow(
static_cast<double>(h_labels_lower_bound[i]),
static_cast<double>(h_labels_upper_bound[i]),
static_cast<double>(h_preds[i])) * wt;
weights_sum += wt;
});
}
exc.Rethrow();
PackedReduceResult res{residue_sum, weights_sum};
return res;
}
@@ -119,25 +123,25 @@ class ElementWiseSurvivalMetricsReduction {
#endif // XGBOOST_USE_CUDA
PackedReduceResult Reduce(
int device,
const GenericParameter &ctx,
const HostDeviceVector<bst_float>& weights,
const HostDeviceVector<bst_float>& labels_lower_bound,
const HostDeviceVector<bst_float>& labels_upper_bound,
const HostDeviceVector<bst_float>& preds) {
PackedReduceResult result;
if (device < 0) {
result = CpuReduceMetrics(weights, labels_lower_bound, labels_upper_bound, preds);
if (ctx.gpu_id < 0) {
result = CpuReduceMetrics(weights, labels_lower_bound, labels_upper_bound,
preds, ctx.Threads());
}
#if defined(XGBOOST_USE_CUDA)
else { // NOLINT
device_ = device;
preds.SetDevice(device_);
labels_lower_bound.SetDevice(device_);
labels_upper_bound.SetDevice(device_);
weights.SetDevice(device_);
preds.SetDevice(ctx.gpu_id);
labels_lower_bound.SetDevice(ctx.gpu_id);
labels_upper_bound.SetDevice(ctx.gpu_id);
weights.SetDevice(ctx.gpu_id);
dh::safe_cuda(cudaSetDevice(device_));
dh::safe_cuda(cudaSetDevice(ctx.gpu_id));
result = DeviceReduceMetrics(weights, labels_lower_bound, labels_upper_bound, preds);
}
#endif // defined(XGBOOST_USE_CUDA)
@@ -146,9 +150,6 @@ class ElementWiseSurvivalMetricsReduction {
private:
EvalRow policy_;
#if defined(XGBOOST_USE_CUDA)
int device_{-1};
#endif // defined(XGBOOST_USE_CUDA)
};
struct EvalIntervalRegressionAccuracy {
@@ -193,18 +194,16 @@ struct EvalAFTNLogLik {
AFTParam param_;
};
template<typename Policy>
struct EvalEWiseSurvivalBase : public Metric {
template <typename Policy> struct EvalEWiseSurvivalBase : public Metric {
explicit EvalEWiseSurvivalBase(GenericParameter const *ctx) {
tparam_ = ctx;
}
EvalEWiseSurvivalBase() = default;
void Configure(const Args& args) override {
policy_.Configure(args);
for (const auto& e : args) {
if (e.first == "gpu_id") {
device_ = dmlc::ParseSignedInt<int>(e.second.c_str(), nullptr, 10);
}
}
reducer_.Configure(policy_);
CHECK(tparam_);
}
bst_float Eval(const HostDeviceVector<bst_float>& preds,
@@ -212,9 +211,10 @@ struct EvalEWiseSurvivalBase : public Metric {
bool distributed) override {
CHECK_EQ(preds.Size(), info.labels_lower_bound_.Size());
CHECK_EQ(preds.Size(), info.labels_upper_bound_.Size());
auto result = reducer_.Reduce(
device_, info.weights_, info.labels_lower_bound_, info.labels_upper_bound_, preds);
CHECK(tparam_);
auto result =
reducer_.Reduce(*tparam_, info.weights_, info.labels_lower_bound_,
info.labels_upper_bound_, preds);
double dat[2] {result.Residue(), result.Weights()};
@@ -252,24 +252,22 @@ struct AFTNLogLikDispatcher : public Metric {
param_.UpdateAllowUnknown(args);
switch (param_.aft_loss_distribution) {
case common::ProbabilityDistributionType::kNormal:
metric_.reset(new EvalEWiseSurvivalBase<EvalAFTNLogLik<common::NormalDistribution>>());
metric_.reset(
new EvalEWiseSurvivalBase<EvalAFTNLogLik<common::NormalDistribution>>(
tparam_));
break;
case common::ProbabilityDistributionType::kLogistic:
metric_.reset(new EvalEWiseSurvivalBase<EvalAFTNLogLik<common::LogisticDistribution>>());
metric_.reset(new EvalEWiseSurvivalBase<
EvalAFTNLogLik<common::LogisticDistribution>>(tparam_));
break;
case common::ProbabilityDistributionType::kExtreme:
metric_.reset(new EvalEWiseSurvivalBase<EvalAFTNLogLik<common::ExtremeDistribution>>());
metric_.reset(new EvalEWiseSurvivalBase<
EvalAFTNLogLik<common::ExtremeDistribution>>(tparam_));
break;
default:
LOG(FATAL) << "Unknown probability distribution";
}
Args new_args{args};
// tparam_ doesn't get propagated to the inner metric object because we didn't use
// Metric::Create(). I don't think it's a good idea to pollute the metric registry with
// specialized versions of the AFT metric, so as a work-around, manually pass the GPU ID
// into the inner metric via configuration.
new_args.emplace_back("gpu_id", std::to_string(tparam_->gpu_id));
metric_->Configure(new_args);
metric_->Configure(args);
}
void SaveConfig(Json* p_out) const override {