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[jvm-packages][xgboost4j-gpu] Support GPU dataframe and DeviceQuantileDMatrix (#7195)

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Following classes are added to support dataframe in java binding:

- `Column` is an abstract type for a single column in tabular data.
- `ColumnBatch` is an abstract type for dataframe.

- `CuDFColumn` is an implementaiton of `Column` that consume cuDF column
- `CudfColumnBatch` is an implementation of `ColumnBatch` that consumes cuDF dataframe.

- `DeviceQuantileDMatrix` is the interface for quantized data.

The Java implementation mimics the Python interface and uses `__cuda_array_interface__` protocol for memory indexing.  One difference is on JVM package, the data batch is staged on the host as java iterators cannot be reset.

Co-authored-by: jiamingy <jm.yuan@outlook.com>
This commit is contained in:
Bobby Wang
2021-09-24 14:25:00 +08:00
committed by GitHub
parent d27a427dc5
commit 0ee11dac77
23 changed files with 1388 additions and 18 deletions
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jvm-packages/xgboost4j-gpu/src/test
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../../xgboost4j/src/test

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jvm-packages/xgboost4j-gpu/src/test/java/ml/dmlc/xgboost4j/gpu/java/BoosterTest.java Normal file
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/*
Copyright (c) 2021 by Contributors
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
package ml.dmlc.xgboost4j.gpu.java;
import java.io.File;
import java.util.HashMap;
import java.util.LinkedList;
import java.util.List;
import java.util.Map;
import junit.framework.TestCase;
import org.junit.Test;
import ai.rapids.cudf.DType;
import ai.rapids.cudf.Schema;
import ai.rapids.cudf.Table;
import ai.rapids.cudf.ColumnVector;
import ai.rapids.cudf.CSVOptions;
import ml.dmlc.xgboost4j.java.Booster;
import ml.dmlc.xgboost4j.java.ColumnBatch;
import ml.dmlc.xgboost4j.java.DMatrix;
import ml.dmlc.xgboost4j.java.DeviceQuantileDMatrix;
import ml.dmlc.xgboost4j.java.XGBoost;
import ml.dmlc.xgboost4j.java.XGBoostError;
/**
* Tests the BoosterTest trained by DMatrix
* @throws XGBoostError
*/
public class BoosterTest {
@Test
public void testBooster() throws XGBoostError {
String trainingDataPath = "../../demo/data/veterans_lung_cancer.csv";
Schema schema = Schema.builder()
.column(DType.FLOAT32, "A")
.column(DType.FLOAT32, "B")
.column(DType.FLOAT32, "C")
.column(DType.FLOAT32, "D")
.column(DType.FLOAT32, "E")
.column(DType.FLOAT32, "F")
.column(DType.FLOAT32, "G")
.column(DType.FLOAT32, "H")
.column(DType.FLOAT32, "I")
.column(DType.FLOAT32, "J")
.column(DType.FLOAT32, "K")
.column(DType.FLOAT32, "L")
.column(DType.FLOAT32, "label")
.build();
CSVOptions opts = CSVOptions.builder()
.hasHeader().build();
int maxBin = 16;
int round = 100;
//set params
Map<String, Object> paramMap = new HashMap<String, Object>() {
{
put("max_depth", 2);
put("objective", "binary:logistic");
put("num_round", round);
put("num_workers", 1);
put("tree_method", "gpu_hist");
put("predictor", "gpu_predictor");
put("max_bin", maxBin);
}
};
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);
}
try (Table X = new Table(df);) {
ColumnVector[] labels = new ColumnVector[1];
labels[0] = tmpTable.getColumn(12);
try (Table y = new Table(labels);) {
CudfColumnBatch batch = new CudfColumnBatch(X, y, null, null);
CudfColumn labelColumn = CudfColumn.from(tmpTable.getColumn(12));
//set watchList
HashMap<String, DMatrix> watches = new HashMap<>();
DMatrix dMatrix1 = new DMatrix(batch, Float.NaN, 1);
dMatrix1.setLabel(labelColumn);
watches.put("train", dMatrix1);
Booster model1 = XGBoost.train(dMatrix1, paramMap, round, watches, null, null);
List<ColumnBatch> tables = new LinkedList<>();
tables.add(batch);
DMatrix incrementalDMatrix = new DeviceQuantileDMatrix(tables.iterator(), Float.NaN, maxBin, 1);
//set watchList
HashMap<String, DMatrix> watches1 = new HashMap<>();
watches1.put("train", incrementalDMatrix);
Booster model2 = XGBoost.train(incrementalDMatrix, paramMap, round, watches1, null, null);
float[][] predicat1 = model1.predict(dMatrix1);
float[][] predicat2 = model2.predict(dMatrix1);
for (int i = 0; i < tmpTable.getRowCount(); i++) {
TestCase.assertTrue(predicat1[i][0] - predicat2[i][0] < 1e-6);
}
}
}
}
}
}

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jvm-packages/xgboost4j-gpu/src/test/java/ml/dmlc/xgboost4j/gpu/java/DMatrixTest.java Normal file
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/*
Copyright (c) 2021 by Contributors
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
package ml.dmlc.xgboost4j.gpu.java;
import java.util.Arrays;
import java.util.LinkedList;
import java.util.List;
import junit.framework.TestCase;
import com.google.common.primitives.Floats;
import org.apache.commons.lang.ArrayUtils;
import org.junit.Test;
import ai.rapids.cudf.Table;
import ml.dmlc.xgboost4j.java.DMatrix;
import ml.dmlc.xgboost4j.java.DeviceQuantileDMatrix;
import ml.dmlc.xgboost4j.java.ColumnBatch;
import ml.dmlc.xgboost4j.java.XGBoostError;
/**
* Test suite for DMatrix based on GPU
*/
public class DMatrixTest {
@Test
public void testCreateFromArrayInterfaceColumns() {
Float[] labelFloats = new Float[]{2f, 4f, 6f, 8f, 10f};
Throwable ex = null;
try (
Table X = new Table.TestBuilder().column(1.f, null, 5.f, 7.f, 9.f).build();
Table y = new Table.TestBuilder().column(labelFloats).build();
Table w = new Table.TestBuilder().column(labelFloats).build();
Table margin = new Table.TestBuilder().column(labelFloats).build();) {
CudfColumnBatch cudfDataFrame = new CudfColumnBatch(X, y, w, null);
CudfColumn labelColumn = CudfColumn.from(y.getColumn(0));
CudfColumn weightColumn = CudfColumn.from(w.getColumn(0));
CudfColumn baseMarginColumn = CudfColumn.from(margin.getColumn(0));
DMatrix dMatrix = new DMatrix(cudfDataFrame, 0, 1);
dMatrix.setLabel(labelColumn);
dMatrix.setWeight(weightColumn);
dMatrix.setBaseMargin(baseMarginColumn);
float[] anchor = convertFloatTofloat(labelFloats);
float[] label = dMatrix.getLabel();
float[] weight = dMatrix.getWeight();
float[] baseMargin = dMatrix.getBaseMargin();
TestCase.assertTrue(Arrays.equals(anchor, label));
TestCase.assertTrue(Arrays.equals(anchor, weight));
TestCase.assertTrue(Arrays.equals(anchor, baseMargin));
} catch (Throwable e) {
ex = e;
e.printStackTrace();
}
TestCase.assertNull(ex);
}
@Test
public void testCreateFromColumnDataIterator() throws XGBoostError {
Float[] label1 = {25f, 21f, 22f, 20f, 24f};
Float[] weight1 = {1.3f, 2.31f, 0.32f, 3.3f, 1.34f};
Float[] baseMargin1 = {1.2f, 0.2f, 1.3f, 2.4f, 3.5f};
Float[] label2 = {9f, 5f, 4f, 10f, 12f};
Float[] weight2 = {3.0f, 1.3f, 3.2f, 0.3f, 1.34f};
Float[] baseMargin2 = {0.2f, 2.5f, 3.1f, 4.4f, 2.2f};
try (
Table X_0 = new Table.TestBuilder()
.column(1.2f, null, 5.2f, 7.2f, 9.2f)
.column(0.2f, 0.4f, 0.6f, 2.6f, 0.10f)
.build();
Table y_0 = new Table.TestBuilder().column(label1).build();
Table w_0 = new Table.TestBuilder().column(weight1).build();
Table m_0 = new Table.TestBuilder().column(baseMargin1).build();
Table X_1 = new Table.TestBuilder().column(11.2f, 11.2f, 15.2f, 17.2f, 19.2f)
.column(1.2f, 1.4f, null, 12.6f, 10.10f).build();
Table y_1 = new Table.TestBuilder().column(label2).build();
Table w_1 = new Table.TestBuilder().column(weight2).build();
Table m_1 = new Table.TestBuilder().column(baseMargin2).build();) {
List<ColumnBatch> tables = new LinkedList<>();
tables.add(new CudfColumnBatch(X_0, y_0, w_0, m_0));
tables.add(new CudfColumnBatch(X_1, y_1, w_1, m_1));
DMatrix dmat = new DeviceQuantileDMatrix(tables.iterator(), 0.0f, 8, 1);
float[] anchorLabel = convertFloatTofloat((Float[]) ArrayUtils.addAll(label1, label2));
float[] anchorWeight = convertFloatTofloat((Float[]) ArrayUtils.addAll(weight1, weight2));
float[] anchorBaseMargin = convertFloatTofloat((Float[]) ArrayUtils.addAll(baseMargin1, baseMargin2));
TestCase.assertTrue(Arrays.equals(anchorLabel, dmat.getLabel()));
TestCase.assertTrue(Arrays.equals(anchorWeight, dmat.getWeight()));
TestCase.assertTrue(Arrays.equals(anchorBaseMargin, dmat.getBaseMargin()));
}
}
private float[] convertFloatTofloat(Float[] in) {
return Floats.toArray(Arrays.asList(in));
}
}