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Fix empty DMatrix with categorical features. (#8739)

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Jiaming Yuan 2023-02-07 00:40:11 +08:00 committed by GitHub
parent 7214a45e83
commit a2e433a089
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GPG Key ID: 4AEE18F83AFDEB23
5 changed files with 93 additions and 11 deletions
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2
python-package/xgboost/dask.py
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@ -641,6 +641,8 @@ class DaskPartitionIter(DataIter): # pylint: disable=R0902
class DaskQuantileDMatrix(DaskDMatrix):
"""A dask version of :py:class:`QuantileDMatrix`."""
@_deprecate_positional_args
def __init__(
self,

5
src/common/quantile.h
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@ -1,5 +1,5 @@
/*!
* Copyright 2014-2022 by XGBoost Contributors
/**
* Copyright 2014-2023 by XGBoost Contributors
* \file quantile.h
* \brief util to compute quantiles
* \author Tianqi Chen
@ -7,7 +7,6 @@
#ifndef XGBOOST_COMMON_QUANTILE_H_
#define XGBOOST_COMMON_QUANTILE_H_
#include <dmlc/base.h>
#include <xgboost/data.h>
#include <xgboost/logging.h>

52
src/data/iterative_dmatrix.cc
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@ -3,15 +3,20 @@
*/
#include "iterative_dmatrix.h"
#include <algorithm> // std::copy
#include <algorithm> // std::copy
#include <cstddef> // std::size_t
#include <type_traits> // std::underlying_type_t
#include <vector> // std::vector
#include "../collective/communicator-inl.h"
#include "../common/categorical.h" // common::IsCat
#include "../common/column_matrix.h"
#include "../tree/param.h" // FIXME(jiamingy): Find a better way to share this parameter.
#include "../tree/param.h" // FIXME(jiamingy): Find a better way to share this parameter.
#include "gradient_index.h"
#include "proxy_dmatrix.h"
#include "simple_batch_iterator.h"
#include "xgboost/data.h" // FeatureType
#include "xgboost/logging.h"
namespace xgboost {
namespace data {
@ -79,6 +84,27 @@ void GetCutsFromRef(std::shared_ptr<DMatrix> ref_, bst_feature_t n_features, Bat
<< "Invalid ref DMatrix, different number of features.";
}
namespace {
// Synchronize feature type in case of empty DMatrix
void SyncFeatureType(std::vector<FeatureType>* p_h_ft) {
if (!collective::IsDistributed()) {
return;
}
auto& h_ft = *p_h_ft;
auto n_ft = h_ft.size();
collective::Allreduce<collective::Operation::kMax>(&n_ft, 1);
if (!h_ft.empty()) {
// Check correct size if this is not an empty DMatrix.
CHECK_EQ(h_ft.size(), n_ft);
}
if (n_ft > 0) {
h_ft.resize(n_ft);
auto ptr = reinterpret_cast<std::underlying_type_t<FeatureType>*>(h_ft.data());
collective::Allreduce<collective::Operation::kMax>(ptr, h_ft.size());
}
}
} // anonymous namespace
void IterativeDMatrix::InitFromCPU(DataIterHandle iter_handle, float missing,
std::shared_ptr<DMatrix> ref) {
DMatrixProxy* proxy = MakeProxy(proxy_);
@ -96,13 +122,14 @@ void IterativeDMatrix::InitFromCPU(DataIterHandle iter_handle, float missing,
return HostAdapterDispatch(proxy, [](auto const& value) { return value.NumCols(); });
};
std::vector<size_t> column_sizes;
std::vector<std::size_t> column_sizes;
auto const is_valid = data::IsValidFunctor{missing};
auto nnz_cnt = [&]() {
return HostAdapterDispatch(proxy, [&](auto const& value) {
size_t n_threads = ctx_.Threads();
size_t n_features = column_sizes.size();
linalg::Tensor<size_t, 2> column_sizes_tloc({n_threads, n_features}, Context::kCpuId);
linalg::Tensor<std::size_t, 2> column_sizes_tloc({n_threads, n_features}, Context::kCpuId);
column_sizes_tloc.Data()->Fill(0ul);
auto view = column_sizes_tloc.HostView();
common::ParallelFor(value.Size(), n_threads, common::Sched::Static(256), [&](auto i) {
auto const& line = value.GetLine(i);
@ -139,7 +166,8 @@ void IterativeDMatrix::InitFromCPU(DataIterHandle iter_handle, float missing,
if (n_features == 0) {
n_features = num_cols();
collective::Allreduce<collective::Operation::kMax>(&n_features, 1);
column_sizes.resize(n_features);
column_sizes.clear();
column_sizes.resize(n_features, 0);
info_.num_col_ = n_features;
} else {
CHECK_EQ(n_features, num_cols()) << "Inconsistent number of columns.";
@ -166,14 +194,18 @@ void IterativeDMatrix::InitFromCPU(DataIterHandle iter_handle, float missing,
* Generate quantiles
*/
accumulated_rows = 0;
std::vector<FeatureType> h_ft;
if (ref) {
GetCutsFromRef(ref, Info().num_col_, batch_param_, &cuts);
h_ft = ref->Info().feature_types.HostVector();
} else {
size_t i = 0;
while (iter.Next()) {
if (!p_sketch) {
h_ft = proxy->Info().feature_types.ConstHostVector();
SyncFeatureType(&h_ft);
p_sketch.reset(new common::HostSketchContainer{
batch_param_.max_bin, proxy->Info().feature_types.ConstHostSpan(), column_sizes, false,
batch_param_.max_bin, h_ft, column_sizes, false,
proxy->Info().data_split_mode == DataSplitMode::kCol, ctx_.Threads()});
}
HostAdapterDispatch(proxy, [&](auto const& batch) {
@ -191,6 +223,9 @@ void IterativeDMatrix::InitFromCPU(DataIterHandle iter_handle, float missing,
CHECK(p_sketch);
p_sketch->MakeCuts(&cuts);
}
if (!h_ft.empty()) {
CHECK_EQ(h_ft.size(), n_features);
}
/**
* Generate gradient index.
@ -202,8 +237,7 @@ void IterativeDMatrix::InitFromCPU(DataIterHandle iter_handle, float missing,
while (iter.Next()) {
HostAdapterDispatch(proxy, [&](auto const& batch) {
proxy->Info().num_nonzero_ = batch_nnz[i];
this->ghist_->PushAdapterBatch(&ctx_, rbegin, prev_sum, batch, missing,
proxy->Info().feature_types.ConstHostSpan(),
this->ghist_->PushAdapterBatch(&ctx_, rbegin, prev_sum, batch, missing, h_ft,
batch_param_.sparse_thresh, Info().num_row_);
});
if (n_batches != 1) {
@ -236,6 +270,8 @@ void IterativeDMatrix::InitFromCPU(DataIterHandle iter_handle, float missing,
this->info_.num_col_ = n_features; // proxy might be empty.
CHECK_EQ(proxy->Info().labels.Size(), 0);
}
Info().feature_types.HostVector() = h_ft;
}
BatchSet<GHistIndexMatrix> IterativeDMatrix::GetGradientIndex(BatchParam const& param) {

21
tests/test_distributed/test_gpu_with_dask/test_gpu_with_dask.py
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@ -265,6 +265,27 @@ class TestDistributedGPU:
) -> None:
run_gpu_hist(params, num_rounds, dataset, dmatrix_type, local_cuda_client)
def test_empty_quantile_dmatrix(self, local_cuda_client: Client) -> None:
client = local_cuda_client
X, y = make_categorical(client, 1, 30, 13)
X_valid, y_valid = make_categorical(client, 10000, 30, 13)
Xy = xgb.dask.DaskQuantileDMatrix(client, X, y, enable_categorical=True)
Xy_valid = xgb.dask.DaskQuantileDMatrix(
client, X_valid, y_valid, ref=Xy, enable_categorical=True
)
result = xgb.dask.train(
client,
{"tree_method": "gpu_hist"},
Xy,
num_boost_round=10,
evals=[(Xy_valid, "Valid")],
)
predt = xgb.dask.inplace_predict(client, result["booster"], X).compute()
np.testing.assert_allclose(y.compute(), predt)
rmse = result["history"]["Valid"]["rmse"][-1]
assert rmse < 32.0
@pytest.mark.skipif(**tm.no_cupy())
def test_dask_array(self, local_cuda_client: Client) -> None:
run_with_dask_array(dxgb.DaskDMatrix, local_cuda_client)

24
tests/test_distributed/test_with_dask/test_with_dask.py
View File

@ -96,6 +96,9 @@ def make_categorical(
l_n_samples = min(
n_samples // n_workers, n_samples - i * (n_samples // n_workers)
)
# make sure there's at least one sample for testing empty DMatrix
if n_samples == 1 and i == 0:
l_n_samples = 1
future = client.submit(
pack,
n_samples=l_n_samples,
@ -1480,6 +1483,27 @@ class TestWithDask:
quantile_hist["Valid"]["rmse"], dmatrix_hist["Valid"]["rmse"]
)
def test_empty_quantile_dmatrix(self, client: Client) -> None:
X, y = make_categorical(client, 2, 30, 13)
X_valid, y_valid = make_categorical(client, 10000, 30, 13)
X_valid, y_valid, _ = deterministic_repartition(client, X_valid, y_valid, None)
Xy = xgb.dask.DaskQuantileDMatrix(client, X, y, enable_categorical=True)
Xy_valid = xgb.dask.DaskQuantileDMatrix(
client, X_valid, y_valid, ref=Xy, enable_categorical=True
)
result = xgb.dask.train(
client,
{"tree_method": "hist"},
Xy,
num_boost_round=10,
evals=[(Xy_valid, "Valid")],
)
predt = xgb.dask.inplace_predict(client, result["booster"], X).compute()
np.testing.assert_allclose(y.compute(), predt)
rmse = result["history"]["Valid"]["rmse"][-1]
assert rmse < 32.0
@given(params=hist_parameter_strategy, dataset=tm.dataset_strategy)
@settings(
deadline=None, max_examples=10, suppress_health_check=suppress, print_blob=True