Check inf in data for all types of DMatrix. (#8911)

jvm-packages/xgboost4j-gpu/src/test/java/ml/dmlc/xgboost4j/gpu/java/BoosterTest.java

@@ -84,9 +84,10 @@ public class BoosterTest {
     };
 
     try (Table tmpTable = Table.readCSV(schema, opts, new File(trainingDataPath))) {
-      ColumnVector[] df = new ColumnVector[12];
-      for (int i = 0; i < 12; ++i) {
-        df[i] = tmpTable.getColumn(i);
+      ColumnVector[] df = new ColumnVector[10];
+      // exclude the first two columns, they are label bounds and contain inf.
+      for (int i = 2; i < 12; ++i) {
+        df[i - 2] = tmpTable.getColumn(i);
       }
       try (Table X = new Table(df);) {
         ColumnVector[] labels = new ColumnVector[1];

jvm-packages/xgboost4j-spark-gpu/src/test/scala/ml/dmlc/xgboost4j/scala/rapids/spark/GpuXGBoostClassifierSuite.scala

@@ -21,7 +21,7 @@ import java.io.File
 import ml.dmlc.xgboost4j.scala.spark.{XGBoostClassificationModel, XGBoostClassifier}
 
 import org.apache.spark.ml.feature.VectorAssembler
-import org.apache.spark.sql.functions.{col, udf}
+import org.apache.spark.sql.functions.{col, udf, when}
 import org.apache.spark.sql.types.{FloatType, StructField, StructType}
 
 class GpuXGBoostClassifierSuite extends GpuTestSuite {
@@ -47,7 +47,8 @@ class GpuXGBoostClassifierSuite extends GpuTestSuite {
         "num_round" -> 10, "num_workers" -> 1, "tree_method" -> "gpu_hist",
         "features_cols" -> featureNames, "label_col" -> labelName)
       val Array(originalDf, testDf) = spark.read.option("header", "true").schema(schema)
-        .csv(dataPath).randomSplit(Array(0.7, 0.3), seed = 1)
+        .csv(dataPath).withColumn("f2", when(col("f2").isin(Float.PositiveInfinity), 0))
+        .randomSplit(Array(0.7, 0.3), seed = 1)
       // Get a model
       val model = new XGBoostClassifier(xgbParam)
         .fit(originalDf)
@@ -64,7 +65,8 @@ class GpuXGBoostClassifierSuite extends GpuTestSuite {
         "num_round" -> 10, "num_workers" -> 1, "tree_method" -> "gpu_hist",
         "features_cols" -> featureNames, "label_col" -> labelName)
       val Array(originalDf, testDf) = spark.read.option("header", "true").schema(schema)
-        .csv(dataPath).randomSplit(Array(0.7, 0.3), seed = 1)
+        .csv(dataPath).withColumn("f2", when(col("f2").isin(Float.PositiveInfinity), 0))
+        .randomSplit(Array(0.7, 0.3), seed = 1)
       val getWeightFromF1 = udf({ f1: Float => if (f1.toInt % 2 == 0) 1.0f else 0.001f })
       val dfWithWeight = originalDf.withColumn("weight", getWeightFromF1(col("f1")))
 
@@ -87,7 +89,8 @@ class GpuXGBoostClassifierSuite extends GpuTestSuite {
       val xgbParam = Map("eta" -> 0.1f, "max_depth" -> 2, "objective" -> "binary:logistic",
         "num_round" -> 10, "num_workers" -> 1)
       val Array(rawInput, testDf) = spark.read.option("header", "true").schema(schema)
-        .csv(dataPath).randomSplit(Array(0.7, 0.3), seed = 1)
+        .csv(dataPath).withColumn("f2", when(col("f2").isin(Float.PositiveInfinity), 0))
+        .randomSplit(Array(0.7, 0.3), seed = 1)
 
       val classifier = new XGBoostClassifier(xgbParam)
         .setFeaturesCol(featureNames)
@@ -122,7 +125,8 @@ class GpuXGBoostClassifierSuite extends GpuTestSuite {
       val xgbParam = Map("eta" -> 0.1f, "max_depth" -> 2, "objective" -> "binary:logistic",
         "num_round" -> 10, "num_workers" -> 1)
       val Array(rawInput, _) = spark.read.option("header", "true").schema(schema)
-        .csv(dataPath).randomSplit(Array(0.7, 0.3), seed = 1)
+        .csv(dataPath).withColumn("f2", when(col("f2").isin(Float.PositiveInfinity), 0))
+        .randomSplit(Array(0.7, 0.3), seed = 1)
 
       val vectorAssembler = new VectorAssembler()
         .setHandleInvalid("keep")
@@ -144,7 +148,8 @@ class GpuXGBoostClassifierSuite extends GpuTestSuite {
     // transform on GPU
     withGpuSparkSession() { spark =>
       val Array(_, testDf) = spark.read.option("header", "true").schema(schema)
-        .csv(dataPath).randomSplit(Array(0.7, 0.3), seed = 1)
+        .csv(dataPath).withColumn("f2", when(col("f2").isin(Float.PositiveInfinity), 0))
+        .randomSplit(Array(0.7, 0.3), seed = 1)
 
       // Since CPU model does not know the information about the features cols that GPU transform
       // pipeline requires. End user needs to setFeaturesCol(features: Array[String]) in the model
@@ -174,7 +179,8 @@ class GpuXGBoostClassifierSuite extends GpuTestSuite {
       val xgbParam = Map("eta" -> 0.1f, "max_depth" -> 2, "objective" -> "binary:logistic",
         "num_round" -> 10, "num_workers" -> 1)
       val Array(rawInput, _) = spark.read.option("header", "true").schema(schema)
-        .csv(dataPath).randomSplit(Array(0.7, 0.3), seed = 1)
+        .csv(dataPath).withColumn("f2", when(col("f2").isin(Float.PositiveInfinity), 0))
+        .randomSplit(Array(0.7, 0.3), seed = 1)
 
       val classifier = new XGBoostClassifier(xgbParam)
         .setFeaturesCol(featureNames)
@@ -190,7 +196,8 @@ class GpuXGBoostClassifierSuite extends GpuTestSuite {
     // transform on CPU
     withCpuSparkSession() { spark =>
       val Array(_, rawInput) = spark.read.option("header", "true").schema(schema)
-        .csv(dataPath).randomSplit(Array(0.7, 0.3), seed = 1)
+        .csv(dataPath).withColumn("f2", when(col("f2").isin(Float.PositiveInfinity), 0))
+        .randomSplit(Array(0.7, 0.3), seed = 1)
 
       val featureColName = "feature_col"
       val vectorAssembler = new VectorAssembler()

python-package/xgboost/testing/data.py

@@ -2,7 +2,10 @@
 from typing import Any, Generator, Tuple, Union
 
 import numpy as np
+import pytest
+from numpy.random import Generator as RNG
 
+import xgboost
 from xgboost.data import pandas_pyarrow_mapper
 
 
@@ -179,3 +182,16 @@ def pd_arrow_dtypes() -> Generator:
         dtype=pd.ArrowDtype(pa.bool_()),
     )
     yield orig, df
+
+
+def check_inf(rng: RNG) -> None:
+    """Validate there's no inf in X."""
+    X = rng.random(size=32).reshape(8, 4)
+    y = rng.random(size=8)
+    X[5, 2] = np.inf
+
+    with pytest.raises(ValueError, match="Input data contains `inf`"):
+        xgboost.QuantileDMatrix(X, y)
+
+    with pytest.raises(ValueError, match="Input data contains `inf`"):
+        xgboost.DMatrix(X, y)

src/common/error_msg.h

@@ -20,5 +20,9 @@ constexpr StringView GroupSize() {
 constexpr StringView LabelScoreSize() {
   return "The size of label doesn't match the size of prediction.";
 }
+
+constexpr StringView InfInData() {
+  return "Input data contains `inf` while `missing` is not set to `inf`";
+}
 }  // namespace xgboost::error
 #endif  // XGBOOST_COMMON_ERROR_MSG_H_

src/data/data.cc

@@ -10,13 +10,16 @@
 #include <cstring>
 
 #include "../collective/communicator-inl.h"
-#include "../common/algorithm.h"  // StableSort
-#include "../common/api_entry.h"  // XGBAPIThreadLocalEntry
+#include "../collective/communicator.h"
+#include "../common/common.h"
+#include "../common/algorithm.h"  // for StableSort
+#include "../common/api_entry.h"  // for XGBAPIThreadLocalEntry
+#include "../common/error_msg.h"  // for InfInData
 #include "../common/group_data.h"
 #include "../common/io.h"
 #include "../common/linalg_op.h"
 #include "../common/math.h"
-#include "../common/numeric.h"  // Iota
+#include "../common/numeric.h"  // for Iota
 #include "../common/threading_utils.h"
 #include "../common/version.h"
 #include "../data/adapter.h"
@@ -1144,7 +1147,7 @@ uint64_t SparsePage::Push(const AdapterBatchT& batch, float missing, int nthread
     });
   }
   exec.Rethrow();
-  CHECK(valid) << "Input data contains `inf` or `nan`";
+  CHECK(valid) << error::InfInData();
   for (const auto & max : max_columns_vector) {
     max_columns = std::max(max_columns, max[0]);
   }

src/data/device_adapter.cuh

@@ -4,7 +4,10 @@
  */
 #ifndef XGBOOST_DATA_DEVICE_ADAPTER_H_
 #define XGBOOST_DATA_DEVICE_ADAPTER_H_
-#include <cstddef>  // for size_t
+#include <thrust/iterator/counting_iterator.h>  // for make_counting_iterator
+#include <thrust/logical.h>                     // for none_of
+
+#include <cstddef>                              // for size_t
 #include <limits>
 #include <memory>
 #include <string>
@@ -213,6 +216,20 @@ size_t GetRowCounts(const AdapterBatchT batch, common::Span<size_t> offset,
                  static_cast<std::size_t>(0), thrust::maximum<size_t>());
   return row_stride;
 }
+
+/**
+ * \brief Check there's no inf in data.
+ */
+template <typename AdapterBatchT>
+bool HasInfInData(AdapterBatchT const& batch, IsValidFunctor is_valid) {
+  auto counting = thrust::make_counting_iterator(0llu);
+  auto value_iter = dh::MakeTransformIterator<float>(
+      counting, [=] XGBOOST_DEVICE(std::size_t idx) { return batch.GetElement(idx).value; });
+  auto valid =
+      thrust::none_of(value_iter, value_iter + batch.Size(),
+                      [is_valid] XGBOOST_DEVICE(float v) { return is_valid(v) && std::isinf(v); });
+  return valid;
+}
 };  // namespace data
 }  // namespace xgboost
 #endif  // XGBOOST_DATA_DEVICE_ADAPTER_H_

src/data/ellpack_page.cu

@@ -1,5 +1,5 @@
-/*!
- * Copyright 2019-2022 XGBoost contributors
+/**
+ * Copyright 2019-2023 by XGBoost contributors
  */
 #include <thrust/iterator/discard_iterator.h>
 #include <thrust/iterator/transform_output_iterator.h>
@@ -9,7 +9,7 @@
 #include "../common/random.h"
 #include "../common/transform_iterator.h"  // MakeIndexTransformIter
 #include "./ellpack_page.cuh"
-#include "device_adapter.cuh"
+#include "device_adapter.cuh"  // for HasInfInData
 #include "gradient_index.h"
 #include "xgboost/data.h"
 
@@ -189,9 +189,8 @@ struct TupleScanOp {
 // Here the data is already correctly ordered and simply needs to be compacted
 // to remove missing data
 template <typename AdapterBatchT>
-void CopyDataToEllpack(const AdapterBatchT &batch,
-                       common::Span<FeatureType const> feature_types,
-                       EllpackPageImpl *dst, int device_idx, float missing) {
+void CopyDataToEllpack(const AdapterBatchT& batch, common::Span<FeatureType const> feature_types,
+                       EllpackPageImpl* dst, int device_idx, float missing) {
   // Some witchcraft happens here
   // The goal is to copy valid elements out of the input to an ELLPACK matrix
   // with a given row stride, using no extra working memory Standard stream
@@ -201,6 +200,9 @@ void CopyDataToEllpack(const AdapterBatchT &batch,
   // correct output position
   auto counting = thrust::make_counting_iterator(0llu);
   data::IsValidFunctor is_valid(missing);
+  bool valid = data::HasInfInData(batch, is_valid);
+  CHECK(valid) << error::InfInData();
+
   auto key_iter = dh::MakeTransformIterator<size_t>(
       counting,
       [=] __device__(size_t idx) {
@@ -239,9 +241,9 @@ void CopyDataToEllpack(const AdapterBatchT &batch,
       cub::DispatchScan<decltype(key_value_index_iter), decltype(out),
                         TupleScanOp<Tuple>, cub::NullType, int64_t>;
 #if THRUST_MAJOR_VERSION >= 2
-  DispatchScan::Dispatch(nullptr, temp_storage_bytes, key_value_index_iter, out,
-                         TupleScanOp<Tuple>(), cub::NullType(), batch.Size(),
-                         nullptr);
+  dh::safe_cuda(DispatchScan::Dispatch(nullptr, temp_storage_bytes, key_value_index_iter, out,
+                                       TupleScanOp<Tuple>(), cub::NullType(), batch.Size(),
+                                       nullptr));
 #else
   DispatchScan::Dispatch(nullptr, temp_storage_bytes, key_value_index_iter, out,
                          TupleScanOp<Tuple>(), cub::NullType(), batch.Size(),
@@ -249,9 +251,9 @@ void CopyDataToEllpack(const AdapterBatchT &batch,
 #endif
   dh::TemporaryArray<char> temp_storage(temp_storage_bytes);
 #if THRUST_MAJOR_VERSION >= 2
-  DispatchScan::Dispatch(temp_storage.data().get(), temp_storage_bytes,
-                         key_value_index_iter, out, TupleScanOp<Tuple>(),
-                         cub::NullType(), batch.Size(), nullptr);
+  dh::safe_cuda(DispatchScan::Dispatch(temp_storage.data().get(), temp_storage_bytes,
+                                       key_value_index_iter, out, TupleScanOp<Tuple>(),
+                                       cub::NullType(), batch.Size(), nullptr));
 #else
   DispatchScan::Dispatch(temp_storage.data().get(), temp_storage_bytes,
                          key_value_index_iter, out, TupleScanOp<Tuple>(),

src/data/gradient_index.h

@@ -1,21 +1,23 @@
-/*!
- * Copyright 2017-2022 by XGBoost Contributors
+/**
+ * Copyright 2017-2023 by XGBoost Contributors
  * \brief Data type for fast histogram aggregation.
  */
 #ifndef XGBOOST_DATA_GRADIENT_INDEX_H_
 #define XGBOOST_DATA_GRADIENT_INDEX_H_
 
-#include <algorithm>  // std::min
-#include <cinttypes>  // std::uint32_t
-#include <cstddef>    // std::size_t
+#include <algorithm>  // for min
+#include <atomic>     // for atomic
+#include <cinttypes>  // for uint32_t
+#include <cstddef>    // for size_t
 #include <memory>
 #include <vector>
 
 #include "../common/categorical.h"
+#include "../common/error_msg.h"  // for InfInData
 #include "../common/hist_util.h"
 #include "../common/numeric.h"
 #include "../common/threading_utils.h"
-#include "../common/transform_iterator.h"  // common::MakeIndexTransformIter
+#include "../common/transform_iterator.h"  // for MakeIndexTransformIter
 #include "adapter.h"
 #include "proxy_dmatrix.h"
 #include "xgboost/base.h"
@@ -62,6 +64,7 @@ class GHistIndexMatrix {
     BinIdxType* index_data = index_data_span.data();
     auto const& ptrs = cut.Ptrs();
     auto const& values = cut.Values();
+    std::atomic<bool> valid{true};
     common::ParallelFor(batch_size, batch_threads, [&](size_t i) {
       auto line = batch.GetLine(i);
       size_t ibegin = row_ptr[rbegin + i];  // index of first entry for current block
@@ -70,6 +73,9 @@ class GHistIndexMatrix {
       for (size_t j = 0; j < line.Size(); ++j) {
         data::COOTuple elem = line.GetElement(j);
         if (is_valid(elem)) {
+          if (XGBOOST_EXPECT((std::isinf(elem.value)), false)) {
+            valid = false;
+          }
           bst_bin_t bin_idx{-1};
           if (common::IsCat(ft, elem.column_idx)) {
             bin_idx = cut.SearchCatBin(elem.value, elem.column_idx, ptrs, values);
@@ -82,6 +88,8 @@ class GHistIndexMatrix {
         }
       }
     });
+
+    CHECK(valid) << error::InfInData();
   }
 
   // Gather hit_count from all threads

src/data/simple_dmatrix.cuh

@@ -1,18 +1,19 @@
-/*!
- * Copyright 2019-2021 by XGBoost Contributors
+/**
+ * Copyright 2019-2023 by XGBoost Contributors
  * \file simple_dmatrix.cuh
  */
 #ifndef XGBOOST_DATA_SIMPLE_DMATRIX_CUH_
 #define XGBOOST_DATA_SIMPLE_DMATRIX_CUH_
 
 #include <thrust/copy.h>
-#include <thrust/scan.h>
 #include <thrust/execution_policy.h>
-#include "device_adapter.cuh"
-#include "../common/device_helpers.cuh"
+#include <thrust/scan.h>
 
-namespace xgboost {
-namespace data {
+#include "../common/device_helpers.cuh"
+#include "../common/error_msg.h"  // for InfInData
+#include "device_adapter.cuh"     // for HasInfInData
+
+namespace xgboost::data {
 
 template <typename AdapterBatchT>
 struct COOToEntryOp {
@@ -61,7 +62,11 @@ void CountRowOffsets(const AdapterBatchT& batch, common::Span<bst_row_t> offset,
 }
 
 template <typename AdapterBatchT>
-size_t CopyToSparsePage(AdapterBatchT const& batch, int32_t device, float missing, SparsePage* page) {
+size_t CopyToSparsePage(AdapterBatchT const& batch, int32_t device, float missing,
+                        SparsePage* page) {
+  bool valid = HasInfInData(batch, IsValidFunctor{missing});
+  CHECK(valid) << error::InfInData();
+
   page->offset.SetDevice(device);
   page->data.SetDevice(device);
   page->offset.Resize(batch.NumRows() + 1);
@@ -73,6 +78,5 @@ size_t CopyToSparsePage(AdapterBatchT const& batch, int32_t device, float missin
 
   return num_nonzero_;
 }
-}  // namespace data
-}  // namespace xgboost
+}  // namespace xgboost::data
 #endif  // XGBOOST_DATA_SIMPLE_DMATRIX_CUH_

tests/python-gpu/test_device_quantile_dmatrix.py

@@ -6,6 +6,7 @@ from hypothesis import given, settings, strategies
 
 import xgboost as xgb
 from xgboost import testing as tm
+from xgboost.testing.data import check_inf
 
 sys.path.append("tests/python")
 import test_quantile_dmatrix as tqd
@@ -153,3 +154,9 @@ class TestQuantileDMatrix:
         from_qdm = xgb.QuantileDMatrix(X, weight=w, ref=Xy_qdm)
 
         assert tm.predictor_equal(from_qdm, from_dm)
+
+    @pytest.mark.skipif(**tm.no_cupy())
+    def test_check_inf(self) -> None:
+        import cupy as cp
+        rng = cp.random.default_rng(1994)
+        check_inf(rng)

tests/python/test_quantile_dmatrix.py

@@ -15,7 +15,7 @@ from xgboost.testing import (
     make_sparse_regression,
     predictor_equal,
 )
-from xgboost.testing.data import np_dtypes
+from xgboost.testing.data import check_inf, np_dtypes
 
 
 class TestQuantileDMatrix:
@@ -244,6 +244,10 @@ class TestQuantileDMatrix:
         from_dm = xgb.QuantileDMatrix(X, weight=w, ref=Xy)
         assert predictor_equal(from_qdm, from_dm)
 
+    def test_check_inf(self) -> None:
+        rng = np.random.default_rng(1994)
+        check_inf(rng)
+
     # we don't test empty Quantile DMatrix in single node construction.
     @given(
         strategies.integers(1, 1000),