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import json
import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
from xgboost.testing.data import run_base_margin_info
cp = pytest.importorskip("cupy")
def test_array_interface() -> None:
arr = cp.array([[1, 2, 3, 4], [1, 2, 3, 4]])
i_arr = arr.__cuda_array_interface__
i_arr = json.loads(json.dumps(i_arr))
ret = xgb.core.from_array_interface(i_arr)
np.testing.assert_equal(cp.asnumpy(arr), cp.asnumpy(ret))
def dmatrix_from_cupy(input_type, DMatrixT, missing=np.nan):
"""Test constructing DMatrix from cupy"""
kRows = 80
kCols = 3
np_X = np.random.randn(kRows, kCols).astype(dtype=input_type)
X = cp.array(np_X)
X[5, 0] = missing
X[3, 1] = missing
y = cp.random.randn(kRows).astype(dtype=input_type)
dtrain = DMatrixT(X, missing=missing, label=y)
assert dtrain.num_col() == kCols
assert dtrain.num_row() == kRows
if DMatrixT is xgb.QuantileDMatrix:
# Slice is not supported by QuantileDMatrix
with pytest.raises(xgb.core.XGBoostError):
dtrain.slice(rindex=[0, 1, 2])
dtrain.slice(rindex=[0, 1, 2])
else:
dtrain.slice(rindex=[0, 1, 2])
dtrain.slice(rindex=[0, 1, 2])
return dtrain
def _test_from_cupy(DMatrixT):
"""Test constructing DMatrix from cupy"""
dmatrix_from_cupy(np.float16, DMatrixT, np.nan)
dmatrix_from_cupy(np.float32, DMatrixT, np.nan)
dmatrix_from_cupy(np.float64, DMatrixT, np.nan)
dmatrix_from_cupy(np.uint8, DMatrixT, 2)
dmatrix_from_cupy(np.uint32, DMatrixT, 3)
dmatrix_from_cupy(np.uint64, DMatrixT, 4)
dmatrix_from_cupy(np.int8, DMatrixT, 2)
dmatrix_from_cupy(np.int32, DMatrixT, -2)
dmatrix_from_cupy(np.int64, DMatrixT, -3)
with pytest.raises(ValueError):
X = cp.random.randn(2, 2, dtype="float32")
y = cp.random.randn(2, 2, 3, dtype="float32")
DMatrixT(X, label=y)
def _test_cupy_training(DMatrixT):
np.random.seed(1)
cp.random.seed(np.uint64(1))
X = cp.random.randn(50, 10, dtype="float32")
y = cp.random.randn(50, dtype="float32")
weights = np.random.random(50) + 1
cupy_weights = cp.array(weights)
base_margin = np.random.random(50)
cupy_base_margin = cp.array(base_margin)
evals_result_cupy = {}
dtrain_cp = DMatrixT(X, y, weight=cupy_weights, base_margin=cupy_base_margin)
params = {"tree_method": "hist", "device": "cuda:0"}
xgb.train(
params, dtrain_cp, evals=[(dtrain_cp, "train")], evals_result=evals_result_cupy
)
evals_result_np = {}
dtrain_np = xgb.DMatrix(
cp.asnumpy(X), cp.asnumpy(y), weight=weights, base_margin=base_margin
)
xgb.train(
params, dtrain_np, evals=[(dtrain_np, "train")], evals_result=evals_result_np
)
assert np.array_equal(
evals_result_cupy["train"]["rmse"], evals_result_np["train"]["rmse"]
)
def _test_cupy_metainfo(DMatrixT):
n = 100
X = np.random.random((n, 2))
dmat_cupy = DMatrixT(cp.array(X))
dmat = xgb.DMatrix(X)
floats = np.random.random(n)
uints = np.array([4, 2, 8]).astype("uint32")
cupy_floats = cp.array(floats)
cupy_uints = cp.array(uints)
dmat.set_float_info("weight", floats)
dmat.set_float_info("label", floats)
dmat.set_float_info("base_margin", floats)
dmat.set_uint_info("group", uints)
dmat_cupy.set_info(weight=cupy_floats)
dmat_cupy.set_info(label=cupy_floats)
dmat_cupy.set_info(base_margin=cupy_floats)
dmat_cupy.set_info(group=cupy_uints)
# Test setting info with cupy
assert np.array_equal(
dmat.get_float_info("weight"), dmat_cupy.get_float_info("weight")
)
assert np.array_equal(
dmat.get_float_info("label"), dmat_cupy.get_float_info("label")
)
assert np.array_equal(
dmat.get_float_info("base_margin"), dmat_cupy.get_float_info("base_margin")
)
assert np.array_equal(
dmat.get_uint_info("group_ptr"), dmat_cupy.get_uint_info("group_ptr")
)
run_base_margin_info(cp.asarray, DMatrixT, "cuda")
@pytest.mark.skipif(**tm.no_cupy())
@pytest.mark.skipif(**tm.no_sklearn())
def test_cupy_training_with_sklearn():
np.random.seed(1)
cp.random.seed(np.uint64(1))
X = cp.random.randn(50, 10, dtype="float32")
y = (cp.random.randn(50, dtype="float32") > 0).astype("int8")
weights = np.random.random(50) + 1
cupy_weights = cp.array(weights)
base_margin = np.random.random(50)
cupy_base_margin = cp.array(base_margin)
clf = xgb.XGBClassifier(tree_method="hist", device="cuda:0")
clf.fit(
X,
y,
sample_weight=cupy_weights,
base_margin=cupy_base_margin,
eval_set=[(X, y)],
)
pred = clf.predict(X)
assert np.array_equal(np.unique(pred), np.array([0, 1]))
class TestFromCupy:
"""Tests for constructing DMatrix from data structure conforming Apache
Arrow specification."""
@pytest.mark.skipif(**tm.no_cupy())
def test_simple_dmat_from_cupy(self):
_test_from_cupy(xgb.DMatrix)
@pytest.mark.skipif(**tm.no_cupy())
def test_device_dmat_from_cupy(self):
_test_from_cupy(xgb.QuantileDMatrix)
@pytest.mark.skipif(**tm.no_cupy())
def test_cupy_training_device_dmat(self):
_test_cupy_training(xgb.QuantileDMatrix)
@pytest.mark.skipif(**tm.no_cupy())
def test_cupy_training_simple_dmat(self):
_test_cupy_training(xgb.DMatrix)
@pytest.mark.skipif(**tm.no_cupy())
def test_cupy_metainfo_simple_dmat(self):
_test_cupy_metainfo(xgb.DMatrix)
@pytest.mark.skipif(**tm.no_cupy())
def test_cupy_metainfo_device_dmat(self):
_test_cupy_metainfo(xgb.QuantileDMatrix)
@pytest.mark.skipif(**tm.no_cupy())
def test_dlpack_simple_dmat(self):
n = 100
X = cp.random.random((n, 2))
xgb.DMatrix(X.toDlpack())
@pytest.mark.skipif(**tm.no_cupy())
def test_cupy_categorical(self):
n_features = 10
X, y = tm.make_categorical(10, n_features, n_categories=4, onehot=False)
X = cp.asarray(X.values.astype(cp.float32))
y = cp.array(y)
feature_types = ["c"] * n_features
assert isinstance(X, cp.ndarray)
Xy = xgb.DMatrix(X, y, feature_types=feature_types)
np.testing.assert_equal(np.array(Xy.feature_types), np.array(feature_types))
@pytest.mark.skipif(**tm.no_cupy())
def test_dlpack_device_dmat(self):
n = 100
X = cp.random.random((n, 2))
m = xgb.QuantileDMatrix(X.toDlpack())
with pytest.raises(
xgb.core.XGBoostError, match="Slicing DMatrix is not supported"
):
m.slice(rindex=[0, 1, 2])
@pytest.mark.skipif(**tm.no_cupy())
def test_qid(self):
rng = cp.random.RandomState(np.uint64(1994))
rows = 100
cols = 10
X, y = rng.randn(rows, cols), rng.randn(rows)
qid = rng.randint(low=0, high=10, size=rows, dtype=np.uint32)
qid = cp.sort(qid)
Xy = xgb.DMatrix(X, y)
Xy.set_info(qid=qid)
group_ptr = Xy.get_uint_info("group_ptr")
assert group_ptr[0] == 0
assert group_ptr[-1] == rows
@pytest.mark.skipif(**tm.no_cupy())
@pytest.mark.mgpu
def test_specified_device(self):
cp.cuda.runtime.setDevice(0)
dtrain = dmatrix_from_cupy(np.float32, xgb.QuantileDMatrix, np.nan)
with pytest.raises(xgb.core.XGBoostError, match="Invalid device ordinal"):
xgb.train(
{"tree_method": "hist", "device": "cuda:1"}, dtrain, num_boost_round=10
)
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