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Reduce time for some multi-gpu tests (#8288)

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* Faster dask tests

* Reuse AllReducer objects in tests.

* Faster boost from prediction tests.

* Use rmm dask fixture.

* Speed up dask demo.

* mypy

* Format with black.

* mypy

* Clang-tidy

Co-authored-by: Hyunsu Philip Cho <chohyu01@cs.washington.edu>
This commit is contained in:
Rory Mitchell
2022-10-04 12:49:33 +02:00
committed by GitHub
parent ca0547bb65
commit d686bf52a6
8 changed files with 337 additions and 336 deletions
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3
src/common/quantile.cu
View File

@@ -508,7 +508,7 @@ void SketchContainer::AllReduce() {
timer_.Start(__func__);
if (!reducer_) {
reducer_ = std::make_unique<dh::AllReducer>();
reducer_ = std::make_shared<dh::AllReducer>();
reducer_->Init(device_);
}
// Reduce the overhead on syncing.
@@ -518,6 +518,7 @@ void SketchContainer::AllReduce() {
std::min(global_sum_rows, static_cast<size_t>(num_bins_ * kFactor));
this->Prune(intermediate_num_cuts);
auto d_columns_ptr = this->columns_ptr_.ConstDeviceSpan();
CHECK_EQ(d_columns_ptr.size(), num_columns_ + 1);
size_t n = d_columns_ptr.size();

54
src/common/quantile.cuh
View File

@@ -37,7 +37,7 @@ class SketchContainer {
private:
Monitor timer_;
std::unique_ptr<dh::AllReducer> reducer_;
std::shared_ptr<dh::AllReducer> reducer_;
HostDeviceVector<FeatureType> feature_types_;
bst_row_t num_rows_;
bst_feature_t num_columns_;
@@ -93,35 +93,37 @@ class SketchContainer {
* \param num_columns Total number of columns in dataset.
* \param num_rows Total number of rows in known dataset (typically the rows in current worker).
* \param device GPU ID.
* \param reducer Optional initialised reducer. Useful for speeding up testing.
*/
SketchContainer(HostDeviceVector<FeatureType> const& feature_types,
int32_t max_bin,
bst_feature_t num_columns, bst_row_t num_rows,
int32_t device)
: num_rows_{num_rows},
num_columns_{num_columns}, num_bins_{max_bin}, device_{device} {
CHECK_GE(device, 0);
// Initialize Sketches for this dmatrix
this->columns_ptr_.SetDevice(device_);
this->columns_ptr_.Resize(num_columns + 1);
this->columns_ptr_b_.SetDevice(device_);
this->columns_ptr_b_.Resize(num_columns + 1);
SketchContainer(HostDeviceVector<FeatureType> const &feature_types,
int32_t max_bin, bst_feature_t num_columns,
bst_row_t num_rows, int32_t device,
std::shared_ptr<dh::AllReducer> reducer = nullptr)
: num_rows_{num_rows},
num_columns_{num_columns}, num_bins_{max_bin}, device_{device},
reducer_(std::move(reducer)) {
CHECK_GE(device, 0);
// Initialize Sketches for this dmatrix
this->columns_ptr_.SetDevice(device_);
this->columns_ptr_.Resize(num_columns + 1);
this->columns_ptr_b_.SetDevice(device_);
this->columns_ptr_b_.Resize(num_columns + 1);
this->feature_types_.Resize(feature_types.Size());
this->feature_types_.Copy(feature_types);
// Pull to device.
this->feature_types_.SetDevice(device);
this->feature_types_.ConstDeviceSpan();
this->feature_types_.ConstHostSpan();
this->feature_types_.Resize(feature_types.Size());
this->feature_types_.Copy(feature_types);
// Pull to device.
this->feature_types_.SetDevice(device);
this->feature_types_.ConstDeviceSpan();
this->feature_types_.ConstHostSpan();
auto d_feature_types = feature_types_.ConstDeviceSpan();
has_categorical_ =
!d_feature_types.empty() &&
thrust::any_of(dh::tbegin(d_feature_types), dh::tend(d_feature_types),
common::IsCatOp{});
auto d_feature_types = feature_types_.ConstDeviceSpan();
has_categorical_ =
!d_feature_types.empty() &&
thrust::any_of(dh::tbegin(d_feature_types), dh::tend(d_feature_types),
common::IsCatOp{});
timer_.Init(__func__);
}
timer_.Init(__func__);
}
/* \brief Return GPU ID for this container. */
int32_t DeviceIdx() const { return device_; }
/* \brief Whether the predictor matrix contains categorical features. */