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Fix clang-tidy warnings. (#4149)

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* Upgrade gtest for clang-tidy.
* Use CMake to install GTest instead of mv.
* Don't enforce clang-tidy to return 0 due to errors in thrust.
* Add a small test for tidy itself.

* Reformat.
This commit is contained in:
Jiaming Yuan
2019-03-13 02:25:51 +08:00
committed by GitHub
parent 259fb809e9
commit 7b9043cf71
41 changed files with 775 additions and 628 deletions
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15
src/linear/updater_coordinate.cc
View File

@@ -30,8 +30,8 @@ class CoordinateUpdater : public LinearUpdater {
tparam_.InitAllowUnknown(args)
};
cparam_.InitAllowUnknown(rest);
selector.reset(FeatureSelector::Create(tparam_.feature_selector));
monitor.Init("CoordinateUpdater");
selector_.reset(FeatureSelector::Create(tparam_.feature_selector));
monitor_.Init("CoordinateUpdater");
}
void Update(HostDeviceVector<GradientPair> *in_gpair, DMatrix *p_fmat,
gbm::GBLinearModel *model, double sum_instance_weight) override {
@@ -48,20 +48,20 @@ class CoordinateUpdater : public LinearUpdater {
dbias, &in_gpair->HostVector(), p_fmat);
}
// prepare for updating the weights
selector->Setup(*model, in_gpair->ConstHostVector(), p_fmat,
selector_->Setup(*model, in_gpair->ConstHostVector(), p_fmat,
tparam_.reg_alpha_denorm,
tparam_.reg_lambda_denorm, cparam_.top_k);
// update weights
for (int group_idx = 0; group_idx < ngroup; ++group_idx) {
for (unsigned i = 0U; i < model->param.num_feature; i++) {
int fidx = selector->NextFeature
int fidx = selector_->NextFeature
(i, *model, group_idx, in_gpair->ConstHostVector(), p_fmat,
tparam_.reg_alpha_denorm, tparam_.reg_lambda_denorm);
if (fidx < 0) break;
this->UpdateFeature(fidx, group_idx, &in_gpair->HostVector(), p_fmat, model);
}
}
monitor.Stop("UpdateFeature");
monitor_.Stop("UpdateFeature");
}
inline void UpdateFeature(int fidx, int group_idx, std::vector<GradientPair> *in_gpair,
@@ -78,11 +78,12 @@ class CoordinateUpdater : public LinearUpdater {
UpdateResidualParallel(fidx, group_idx, ngroup, dw, in_gpair, p_fmat);
}
private:
CoordinateParam cparam_;
// training parameter
LinearTrainParam tparam_;
std::unique_ptr<FeatureSelector> selector;
common::Monitor monitor;
std::unique_ptr<FeatureSelector> selector_;
common::Monitor monitor_;
};
XGBOOST_REGISTER_LINEAR_UPDATER(CoordinateUpdater, "coord_descent")

53
src/linear/updater_gpu_coordinate.cu
View File

@@ -62,7 +62,7 @@ class DeviceShard {
auto column_end =
std::lower_bound(col.cbegin(), col.cend(),
xgboost::Entry(row_end, 0.0f), cmp);
column_segments.push_back(
column_segments.emplace_back(
std::make_pair(column_begin - col.cbegin(), column_end - col.cbegin()));
row_ptr_.push_back(row_ptr_.back() + (column_end - column_begin));
}
@@ -154,13 +154,13 @@ class GPUCoordinateUpdater : public LinearUpdater {
void Init(
const std::vector<std::pair<std::string, std::string>> &args) override {
tparam_.InitAllowUnknown(args);
selector.reset(FeatureSelector::Create(tparam_.feature_selector));
monitor.Init("GPUCoordinateUpdater");
selector_.reset(FeatureSelector::Create(tparam_.feature_selector));
monitor_.Init("GPUCoordinateUpdater");
}
void LazyInitShards(DMatrix *p_fmat,
const gbm::GBLinearModelParam &model_param) {
if (!shards.empty()) return;
if (!shards_.empty()) return;
dist_ = GPUDistribution::Block(GPUSet::All(tparam_.gpu_id, tparam_.n_gpus,
p_fmat->Info().num_row_));
@@ -183,9 +183,9 @@ class GPUCoordinateUpdater : public LinearUpdater {
CHECK(p_fmat->SingleColBlock());
SparsePage const& batch = *(p_fmat->GetColumnBatches().begin());
shards.resize(n_devices);
shards_.resize(n_devices);
// Create device shards
dh::ExecuteIndexShards(&shards,
dh::ExecuteIndexShards(&shards_,
[&](int i, std::unique_ptr<DeviceShard>& shard) {
shard = std::unique_ptr<DeviceShard>(
new DeviceShard(devices.DeviceId(i), batch, row_segments[i],
@@ -196,38 +196,38 @@ class GPUCoordinateUpdater : public LinearUpdater {
void Update(HostDeviceVector<GradientPair> *in_gpair, DMatrix *p_fmat,
gbm::GBLinearModel *model, double sum_instance_weight) override {
tparam_.DenormalizePenalties(sum_instance_weight);
monitor.Start("LazyInitShards");
monitor_.Start("LazyInitShards");
this->LazyInitShards(p_fmat, model->param);
monitor.Stop("LazyInitShards");
monitor_.Stop("LazyInitShards");
monitor.Start("UpdateGpair");
monitor_.Start("UpdateGpair");
// Update gpair
dh::ExecuteIndexShards(&shards, [&](int idx, std::unique_ptr<DeviceShard>& shard) {
dh::ExecuteIndexShards(&shards_, [&](int idx, std::unique_ptr<DeviceShard>& shard) {
if (!shard->IsEmpty()) {
shard->UpdateGpair(in_gpair->ConstHostVector(), model->param);
}
});
monitor.Stop("UpdateGpair");
monitor_.Stop("UpdateGpair");
monitor.Start("UpdateBias");
monitor_.Start("UpdateBias");
this->UpdateBias(p_fmat, model);
monitor.Stop("UpdateBias");
monitor_.Stop("UpdateBias");
// prepare for updating the weights
selector->Setup(*model, in_gpair->ConstHostVector(), p_fmat,
tparam_.reg_alpha_denorm, tparam_.reg_lambda_denorm,
coord_param_.top_k);
monitor.Start("UpdateFeature");
selector_->Setup(*model, in_gpair->ConstHostVector(), p_fmat,
tparam_.reg_alpha_denorm, tparam_.reg_lambda_denorm,
coord_param_.top_k);
monitor_.Start("UpdateFeature");
for (auto group_idx = 0; group_idx < model->param.num_output_group;
++group_idx) {
for (auto i = 0U; i < model->param.num_feature; i++) {
auto fidx = selector->NextFeature(
auto fidx = selector_->NextFeature(
i, *model, group_idx, in_gpair->ConstHostVector(), p_fmat,
tparam_.reg_alpha_denorm, tparam_.reg_lambda_denorm);
if (fidx < 0) break;
this->UpdateFeature(fidx, group_idx, &in_gpair->HostVector(), model);
}
}
monitor.Stop("UpdateFeature");
monitor_.Stop("UpdateFeature");
}
void UpdateBias(DMatrix *p_fmat, gbm::GBLinearModel *model) {
@@ -235,7 +235,7 @@ class GPUCoordinateUpdater : public LinearUpdater {
++group_idx) {
// Get gradient
auto grad = dh::ReduceShards<GradientPair>(
&shards, [&](std::unique_ptr<DeviceShard> &shard) {
&shards_, [&](std::unique_ptr<DeviceShard> &shard) {
if (!shard->IsEmpty()) {
GradientPair result =
shard->GetBiasGradient(group_idx,
@@ -251,7 +251,7 @@ class GPUCoordinateUpdater : public LinearUpdater {
model->bias()[group_idx] += dbias;
// Update residual
dh::ExecuteIndexShards(&shards, [&](int idx, std::unique_ptr<DeviceShard>& shard) {
dh::ExecuteIndexShards(&shards_, [&](int idx, std::unique_ptr<DeviceShard>& shard) {
if (!shard->IsEmpty()) {
shard->UpdateBiasResidual(dbias, group_idx,
model->param.num_output_group);
@@ -266,7 +266,7 @@ class GPUCoordinateUpdater : public LinearUpdater {
bst_float &w = (*model)[fidx][group_idx];
// Get gradient
auto grad = dh::ReduceShards<GradientPair>(
&shards, [&](std::unique_ptr<DeviceShard> &shard) {
&shards_, [&](std::unique_ptr<DeviceShard> &shard) {
if (!shard->IsEmpty()) {
return shard->GetGradient(group_idx, model->param.num_output_group,
fidx);
@@ -280,7 +280,7 @@ class GPUCoordinateUpdater : public LinearUpdater {
tparam_.reg_lambda_denorm));
w += dw;
dh::ExecuteIndexShards(&shards, [&](int idx,
dh::ExecuteIndexShards(&shards_, [&](int idx,
std::unique_ptr<DeviceShard> &shard) {
if (!shard->IsEmpty()) {
shard->UpdateResidual(dw, group_idx, model->param.num_output_group, fidx);
@@ -288,14 +288,15 @@ class GPUCoordinateUpdater : public LinearUpdater {
});
}
private:
// training parameter
LinearTrainParam tparam_;
CoordinateParam coord_param_;
GPUDistribution dist_;
std::unique_ptr<FeatureSelector> selector;
common::Monitor monitor;
std::unique_ptr<FeatureSelector> selector_;
common::Monitor monitor_;
std::vector<std::unique_ptr<DeviceShard>> shards;
std::vector<std::unique_ptr<DeviceShard>> shards_;
};
XGBOOST_REGISTER_LINEAR_UPDATER(GPUCoordinateUpdater, "gpu_coord_descent")